def test_LinearKernel_getDimension():
kernel = Kernel.LinearKernel
statement = "The method _getDimension on LinearKernel will return the dimension of the data after the linear " + \
"expansion function is applied"
expected_result = 5
actual_result = kernel._getDimension(1, (5, 0))
testFunction(statement, expected_result, actual_result)
expected_result = 7
actual_result = kernel._getDimension(1, (7, 20))
testFunction(statement, expected_result, actual_result)
def test_QuadraticKernel_getDimension():
kernel = Kernel.QuadraticKernel
statement = "The method _getDimension on QuadraticKernel will return the dimension of the data after a " + \
"quadratic expansion function is applied"
expected_result = 5
actual_result = kernel._getDimension(2, (2, 0))
testFunction(statement, expected_result, actual_result)
expected_result = (7 + 6 + 5 + 4 + 3 + 2 + 1) + 7
actual_result = kernel._getDimension(2, (7, 20))
testFunction(statement, expected_result, actual_result)
def test_LinearKernel_expansion():
kernel = Kernel.LinearKernel
statement = "The method _expansion on LinearKernel will augment the input with a constant and the matrix of " + \
"the kernel of two matrices will agree with the dot product of their expansions"
A = np.array([[1], [2], [3]])
expected_result = np.r_[[[1]], A]
actual_result = kernel._expansion(1, 1, A)
testFunction(statement, expected_result, actual_result)
A = np.arange(200).reshape(10, 20) / 10
B = np.arange(20, 180).reshape(10, 16) / 10
expected_result = (kernel._expansion(1, 1, A)).T @ (kernel._expansion(
1, 1, B))
actual_result = kernel._getMatrix(1, 1, A, B)
testFunction(statement, expected_result, actual_result)
def test_QuadraticKernel_expansion():
kernel = Kernel.QuadraticKernel
statement = "The quadratic expansion will return a constant in the first row, and then the original data, and " + \
"then all combinations of two rows in the original data. All combinations except the constant and " + \
"the perfect squares are multiplied by the constant sqrt(2)"
A = np.array([[1, 2, 3]])
expected_result = np.array([[1, 1, 1], (np.array([1, 2, 3]) * np.sqrt(2)),
[1, 4, 9]])
actual_result = kernel._expansion(2, 1, A)
testFunction(statement, expected_result, actual_result)
A = np.array([[2, 4, 6], [1, 0, 9]])
expected_result = np.array([[1, 1, 1], (np.array([2, 4, 6]) * np.sqrt(2)),
np.array([1, 0, 9]) * np.sqrt(2), [4, 16, 36],
np.array([2, 0, 54]) * np.sqrt(2), [1, 0, 81]])
actual_result = kernel._expansion(2, 1, A)
testFunction(statement, expected_result, actual_result)
def test_QuadraticKernel_getMatrix():
kernel = Kernel.QuadraticKernel
statement = "The method _getMatrix on QuadraticKernel will return a matrix(i, j) containing the element-wise " + \
"square of 1 + the dot products of the ith column of the left matrix with the jth column of the " + \
"right matrix"
expected_result = "ValueError"
try:
actual_result = kernel._getMatrix(2, 1, np.array([[1], [2]]),
np.array([[1], [2], [3]]))
except ValueError:
actual_result = "ValueError"
testFunction(statement, expected_result, actual_result)
expected_result = (1 + np.array([[14]]))**2
actual_result = kernel._getMatrix(2, 1, np.array([[1], [2], [3]]),
np.array([[1], [2], [3]]))
testFunction(statement, expected_result, actual_result)
A = np.arange(200).reshape(10, 20)
B = np.arange(20, 180).reshape(10, 16)
expected_result = (kernel._expansion(2, 1, A)).T @ (kernel._expansion(
2, 1, B))
actual_result = kernel._getMatrix(2, 1, A, B)
testFunction(statement, expected_result, actual_result)
A = np.arange(200).reshape(
10, 20) + 1.0j * np.arange(200, 400).reshape(10, 20)
B = np.arange(20, 180).reshape(
10, 16) + 1.0j * np.arange(160, 320).reshape(10, 16)
expected_result = (kernel._expansion(
2, 1, A)).conj().T @ (kernel._expansion(2, 1, B))
actual_result = kernel._getMatrix(2, 1, A, B)
testFunction(statement, expected_result, actual_result)
A = np.arange(200).reshape(
10, 20) + 1.0j * np.arange(200, 400).reshape(10, 20)
B = np.arange(20, 180).reshape(
10, 16) + 1.0j * np.arange(160, 320).reshape(10, 16)
expected_result = np.diag(
(kernel._expansion(2, 1, A)).conj().T @ (kernel._expansion(2, 1, B)))
actual_result = kernel._getMatrix(2, 1, A, B, only_diag=True)
testFunction(statement, expected_result, actual_result)
def test_QuadraticKernel_getGradient():
kernel = Kernel.QuadraticKernel
statement = "The method _getGradient on Quadratic will return a 3tensor containing one gradient vector for " + \
"each pair of columns of the two input matrices, i and j, respectively (tensor[:, i, j])"
expected_result = "ValueError"
try:
actual_result = kernel._getGradient(2, 1, np.array([[1], [2]]),
np.array([[1], [2], [3]]))
except ValueError:
actual_result = "ValueError"
testFunction(statement, expected_result, actual_result)
A = np.array([[4], [5], [6]])
B = np.array([[1], [2], [3]])
expected_result = np.einsum('ij,...j->i...j', B, 2 * (1 + A.T @ B))
actual_result = kernel._getGradient(2, 1, A, B)
testFunction(statement, expected_result, actual_result)
A = np.arange(200).reshape(10, 20)
B = np.arange(20, 180).reshape(10, 16)
expected_result = np.einsum('ij,...j->i...j', B, 2 * (1 + A.T @ B))
actual_result = kernel._getGradient(2, 1, A, B)
testFunction(statement, expected_result, actual_result)
A = np.arange(200).reshape(
10, 20) + 1.0j * np.arange(200, 400).reshape(10, 20)
B = np.arange(20, 180).reshape(
10, 16) + 1.0j * np.arange(160, 320).reshape(10, 16)
expected_result = np.einsum('ij,...j->i...j', B, 2 * (1 + A.conj().T @ B))
actual_result = kernel._getGradient(2, 1, A, B)
testFunction(statement, expected_result, actual_result)
A = np.arange(200).reshape(
10, 20) + 1.0j * np.arange(200, 400).reshape(10, 20)
B = np.arange(20, 180).reshape(
10, 16) + 1.0j * np.arange(160, 320).reshape(10, 16)
expected_result = np.einsum('ij,...j->i...j', B,
np.diag(2 * (1 + A.conj().T @ B)))
actual_result = kernel._getGradient(2, 1, A, B, only_diag=True)
testFunction(statement, expected_result, actual_result)
def test_LinearKernel_getGradient():
kernel = Kernel.LinearKernel
statement = "The method _getGradient on LinearKernel will return a 3tensor containing one gradient vector for " + \
"each pair of columns of the two input matrices, i and j, respectively (tensor[:, i, j])"
expected_result = "ValueError"
try:
actual_result = kernel._getGradient(1, 1, np.array([[1], [2]]),
np.array([[1], [2], [3]]))
except ValueError:
actual_result = "ValueError"
testFunction(statement, expected_result, actual_result)
expected_result = np.array([[[1]], [[2]], [[3]]])
actual_result = kernel._getGradient(1, 1, np.array([[4], [5], [6]]),
np.array([[1], [2], [3]]))
testFunction(statement, expected_result, actual_result)
A = np.arange(200).reshape(10, 20)
B = np.arange(20, 180).reshape(10, 16)
expected_result = np.repeat(B.copy(), 20, axis=0).reshape(10, 20, 16)
actual_result = kernel._getGradient(1, 1, A, B)
testFunction(statement, expected_result, actual_result)
A = np.arange(200).reshape(
10, 20) + 1.0j * np.arange(200, 400).reshape(10, 20)
B = np.arange(20, 180).reshape(
10, 16) + 1.0j * np.arange(160, 320).reshape(10, 16)
expected_result = np.repeat(B.copy(), 20, axis=0).reshape(10, 20, 16)
actual_result = kernel._getGradient(1, 1, A, B)
testFunction(statement, expected_result, actual_result)
A = np.arange(200).reshape(10, 20)
B = np.arange(20, 180).reshape(10, 16)
expected_result = B.copy().reshape(10, 1, 16)
actual_result = kernel._getGradient(1, 1, A, B, only_diag=True)
def test_LinearKernel_getMatrix():
kernel = Kernel.LinearKernel
statement = "The method _getMatrix on LinearKernel will return a matrix(i, j) containing 1 + the result of dot " + \
"products of the ith column of the left matrix with the jth column of the right matrix"
expected_result = "ValueError"
try:
actual_result = kernel._getMatrix(1, 1, np.array([[1], [2]]),
np.array([[1], [2], [3]]))
except ValueError:
actual_result = "ValueError"
testFunction(statement, expected_result, actual_result)
expected_result = 1 + np.array([[14]])
actual_result = kernel._getMatrix(1, 1, np.array([[1], [2], [3]]),
np.array([[1], [2], [3]]))
testFunction(statement, expected_result, actual_result)
A = np.arange(200).reshape(10, 20)
B = np.arange(20, 180).reshape(10, 16)
C = np.array(
[[
109200, 110100, 111000, 111900, 112800, 113700, 114600, 115500,
116400, 117300, 118200, 119100, 120000, 120900, 121800, 122700
],
[
110120, 111030, 111940, 112850, 113760, 114670, 115580, 116490,
117400, 118310, 119220, 120130, 121040, 121950, 122860, 123770
],
[
111040, 111960, 112880, 113800, 114720, 115640, 116560, 117480,
118400, 119320, 120240, 121160, 122080, 123000, 123920, 124840
],
[
111960, 112890, 113820, 114750, 115680, 116610, 117540, 118470,
119400, 120330, 121260, 122190, 123120, 124050, 124980, 125910
],
[
112880, 113820, 114760, 115700, 116640, 117580, 118520, 119460,
120400, 121340, 122280, 123220, 124160, 125100, 126040, 126980
],
[
113800, 114750, 115700, 116650, 117600, 118550, 119500, 120450,
121400, 122350, 123300, 124250, 125200, 126150, 127100, 128050
],
[
114720, 115680, 116640, 117600, 118560, 119520, 120480, 121440,
122400, 123360, 124320, 125280, 126240, 127200, 128160, 129120
],
[
115640, 116610, 117580, 118550, 119520, 120490, 121460, 122430,
123400, 124370, 125340, 126310, 127280, 128250, 129220, 130190
],
[
116560, 117540, 118520, 119500, 120480, 121460, 122440, 123420,
124400, 125380, 126360, 127340, 128320, 129300, 130280, 131260
],
[
117480, 118470, 119460, 120450, 121440, 122430, 123420, 124410,
125400, 126390, 127380, 128370, 129360, 130350, 131340, 132330
],
[
118400, 119400, 120400, 121400, 122400, 123400, 124400, 125400,
126400, 127400, 128400, 129400, 130400, 131400, 132400, 133400
],
[
119320, 120330, 121340, 122350, 123360, 124370, 125380, 126390,
127400, 128410, 129420, 130430, 131440, 132450, 133460, 134470
],
[
120240, 121260, 122280, 123300, 124320, 125340, 126360, 127380,
128400, 129420, 130440, 131460, 132480, 133500, 134520, 135540
],
[
121160, 122190, 123220, 124250, 125280, 126310, 127340, 128370,
129400, 130430, 131460, 132490, 133520, 134550, 135580, 136610
],
[
122080, 123120, 124160, 125200, 126240, 127280, 128320, 129360,
130400, 131440, 132480, 133520, 134560, 135600, 136640, 137680
],
[
123000, 124050, 125100, 126150, 127200, 128250, 129300, 130350,
131400, 132450, 133500, 134550, 135600, 136650, 137700, 138750
],
[
123920, 124980, 126040, 127100, 128160, 129220, 130280, 131340,
132400, 133460, 134520, 135580, 136640, 137700, 138760, 139820
],
[
124840, 125910, 126980, 128050, 129120, 130190, 131260, 132330,
133400, 134470, 135540, 136610, 137680, 138750, 139820, 140890
],
[
125760, 126840, 127920, 129000, 130080, 131160, 132240, 133320,
134400, 135480, 136560, 137640, 138720, 139800, 140880, 141960
],
[
126680, 127770, 128860, 129950, 131040, 132130, 133220, 134310,
135400, 136490, 137580, 138670, 139760, 140850, 141940, 143030
]])
expected_result = 1 + C
actual_result = kernel._getMatrix(1, 1, A, B)
A = np.arange(200).reshape(
10, 20) + 1.0j * np.arange(200, 400).reshape(10, 20)
B = np.arange(20, 180).reshape(
10, 16) + 1.0j * np.arange(160, 320).reshape(10, 16)
C = np.array([[
808400. - 58000.j, 812200. - 60000.j, 816000. - 62000.j,
819800. - 64000.j, 823600. - 66000.j, 827400. - 68000.j,
831200. - 70000.j, 835000. - 72000.j, 838800. - 74000.j,
842600. - 76000.j, 846400. - 78000.j, 850200. - 80000.j,
854000. - 82000.j, 857800. - 84000.j, 861600. - 86000.j,
865400. - 88000.j
],
[
811640. - 56600.j, 815460. - 58600.j, 819280. - 60600.j,
823100. - 62600.j, 826920. - 64600.j, 830740. - 66600.j,
834560. - 68600.j, 838380. - 70600.j, 842200. - 72600.j,
846020. - 74600.j, 849840. - 76600.j, 853660. - 78600.j,
857480. - 80600.j, 861300. - 82600.j, 865120. - 84600.j,
868940. - 86600.j
],
[
814880. - 55200.j, 818720. - 57200.j, 822560. - 59200.j,
826400. - 61200.j, 830240. - 63200.j, 834080. - 65200.j,
837920. - 67200.j, 841760. - 69200.j, 845600. - 71200.j,
849440. - 73200.j, 853280. - 75200.j, 857120. - 77200.j,
860960. - 79200.j, 864800. - 81200.j, 868640. - 83200.j,
872480. - 85200.j
],
[
818120. - 53800.j, 821980. - 55800.j, 825840. - 57800.j,
829700. - 59800.j, 833560. - 61800.j, 837420. - 63800.j,
841280. - 65800.j, 845140. - 67800.j, 849000. - 69800.j,
852860. - 71800.j, 856720. - 73800.j, 860580. - 75800.j,
864440. - 77800.j, 868300. - 79800.j, 872160. - 81800.j,
876020. - 83800.j
],
[
821360. - 52400.j, 825240. - 54400.j, 829120. - 56400.j,
833000. - 58400.j, 836880. - 60400.j, 840760. - 62400.j,
844640. - 64400.j, 848520. - 66400.j, 852400. - 68400.j,
856280. - 70400.j, 860160. - 72400.j, 864040. - 74400.j,
867920. - 76400.j, 871800. - 78400.j, 875680. - 80400.j,
879560. - 82400.j
],
[
824600. - 51000.j, 828500. - 53000.j, 832400. - 55000.j,
836300. - 57000.j, 840200. - 59000.j, 844100. - 61000.j,
848000. - 63000.j, 851900. - 65000.j, 855800. - 67000.j,
859700. - 69000.j, 863600. - 71000.j, 867500. - 73000.j,
871400. - 75000.j, 875300. - 77000.j, 879200. - 79000.j,
883100. - 81000.j
],
[
827840. - 49600.j, 831760. - 51600.j, 835680. - 53600.j,
839600. - 55600.j, 843520. - 57600.j, 847440. - 59600.j,
851360. - 61600.j, 855280. - 63600.j, 859200. - 65600.j,
863120. - 67600.j, 867040. - 69600.j, 870960. - 71600.j,
874880. - 73600.j, 878800. - 75600.j, 882720. - 77600.j,
886640. - 79600.j
],
[
831080. - 48200.j, 835020. - 50200.j, 838960. - 52200.j,
842900. - 54200.j, 846840. - 56200.j, 850780. - 58200.j,
854720. - 60200.j, 858660. - 62200.j, 862600. - 64200.j,
866540. - 66200.j, 870480. - 68200.j, 874420. - 70200.j,
878360. - 72200.j, 882300. - 74200.j, 886240. - 76200.j,
890180. - 78200.j
],
[
834320. - 46800.j, 838280. - 48800.j, 842240. - 50800.j,
846200. - 52800.j, 850160. - 54800.j, 854120. - 56800.j,
858080. - 58800.j, 862040. - 60800.j, 866000. - 62800.j,
869960. - 64800.j, 873920. - 66800.j, 877880. - 68800.j,
881840. - 70800.j, 885800. - 72800.j, 889760. - 74800.j,
893720. - 76800.j
],
[
837560. - 45400.j, 841540. - 47400.j, 845520. - 49400.j,
849500. - 51400.j, 853480. - 53400.j, 857460. - 55400.j,
861440. - 57400.j, 865420. - 59400.j, 869400. - 61400.j,
873380. - 63400.j, 877360. - 65400.j, 881340. - 67400.j,
885320. - 69400.j, 889300. - 71400.j, 893280. - 73400.j,
897260. - 75400.j
],
[
840800. - 44000.j, 844800. - 46000.j, 848800. - 48000.j,
852800. - 50000.j, 856800. - 52000.j, 860800. - 54000.j,
864800. - 56000.j, 868800. - 58000.j, 872800. - 60000.j,
876800. - 62000.j, 880800. - 64000.j, 884800. - 66000.j,
888800. - 68000.j, 892800. - 70000.j, 896800. - 72000.j,
900800. - 74000.j
],
[
844040. - 42600.j, 848060. - 44600.j, 852080. - 46600.j,
856100. - 48600.j, 860120. - 50600.j, 864140. - 52600.j,
868160. - 54600.j, 872180. - 56600.j, 876200. - 58600.j,
880220. - 60600.j, 884240. - 62600.j, 888260. - 64600.j,
892280. - 66600.j, 896300. - 68600.j, 900320. - 70600.j,
904340. - 72600.j
],
[
847280. - 41200.j, 851320. - 43200.j, 855360. - 45200.j,
859400. - 47200.j, 863440. - 49200.j, 867480. - 51200.j,
871520. - 53200.j, 875560. - 55200.j, 879600. - 57200.j,
883640. - 59200.j, 887680. - 61200.j, 891720. - 63200.j,
895760. - 65200.j, 899800. - 67200.j, 903840. - 69200.j,
907880. - 71200.j
],
[
850520. - 39800.j, 854580. - 41800.j, 858640. - 43800.j,
862700. - 45800.j, 866760. - 47800.j, 870820. - 49800.j,
874880. - 51800.j, 878940. - 53800.j, 883000. - 55800.j,
887060. - 57800.j, 891120. - 59800.j, 895180. - 61800.j,
899240. - 63800.j, 903300. - 65800.j, 907360. - 67800.j,
911420. - 69800.j
],
[
853760. - 38400.j, 857840. - 40400.j, 861920. - 42400.j,
866000. - 44400.j, 870080. - 46400.j, 874160. - 48400.j,
878240. - 50400.j, 882320. - 52400.j, 886400. - 54400.j,
890480. - 56400.j, 894560. - 58400.j, 898640. - 60400.j,
902720. - 62400.j, 906800. - 64400.j, 910880. - 66400.j,
914960. - 68400.j
],
[
857000. - 37000.j, 861100. - 39000.j, 865200. - 41000.j,
869300. - 43000.j, 873400. - 45000.j, 877500. - 47000.j,
881600. - 49000.j, 885700. - 51000.j, 889800. - 53000.j,
893900. - 55000.j, 898000. - 57000.j, 902100. - 59000.j,
906200. - 61000.j, 910300. - 63000.j, 914400. - 65000.j,
918500. - 67000.j
],
[
860240. - 35600.j, 864360. - 37600.j, 868480. - 39600.j,
872600. - 41600.j, 876720. - 43600.j, 880840. - 45600.j,
884960. - 47600.j, 889080. - 49600.j, 893200. - 51600.j,
897320. - 53600.j, 901440. - 55600.j, 905560. - 57600.j,
909680. - 59600.j, 913800. - 61600.j, 917920. - 63600.j,
922040. - 65600.j
],
[
863480. - 34200.j, 867620. - 36200.j, 871760. - 38200.j,
875900. - 40200.j, 880040. - 42200.j, 884180. - 44200.j,
888320. - 46200.j, 892460. - 48200.j, 896600. - 50200.j,
900740. - 52200.j, 904880. - 54200.j, 909020. - 56200.j,
913160. - 58200.j, 917300. - 60200.j, 921440. - 62200.j,
925580. - 64200.j
],
[
866720. - 32800.j, 870880. - 34800.j, 875040. - 36800.j,
879200. - 38800.j, 883360. - 40800.j, 887520. - 42800.j,
891680. - 44800.j, 895840. - 46800.j, 900000. - 48800.j,
904160. - 50800.j, 908320. - 52800.j, 912480. - 54800.j,
916640. - 56800.j, 920800. - 58800.j, 924960. - 60800.j,
929120. - 62800.j
],
[
869960. - 31400.j, 874140. - 33400.j, 878320. - 35400.j,
882500. - 37400.j, 886680. - 39400.j, 890860. - 41400.j,
895040. - 43400.j, 899220. - 45400.j, 903400. - 47400.j,
907580. - 49400.j, 911760. - 51400.j, 915940. - 53400.j,
920120. - 55400.j, 924300. - 57400.j, 928480. - 59400.j,
932660. - 61400.j
]])
expected_result = 1 + C
actual_result = kernel._getMatrix(1, 1, A, B)
testFunction(statement, expected_result, actual_result)
A = np.arange(200).reshape(10, 20)
B = np.arange(20, 180).reshape(10, 16)
C = np.array(
[[
109200, 110100, 111000, 111900, 112800, 113700, 114600, 115500,
116400, 117300, 118200, 119100, 120000, 120900, 121800, 122700
],
[
110120, 111030, 111940, 112850, 113760, 114670, 115580, 116490,
117400, 118310, 119220, 120130, 121040, 121950, 122860, 123770
],
[
111040, 111960, 112880, 113800, 114720, 115640, 116560, 117480,
118400, 119320, 120240, 121160, 122080, 123000, 123920, 124840
],
[
111960, 112890, 113820, 114750, 115680, 116610, 117540, 118470,
119400, 120330, 121260, 122190, 123120, 124050, 124980, 125910
],
[
112880, 113820, 114760, 115700, 116640, 117580, 118520, 119460,
120400, 121340, 122280, 123220, 124160, 125100, 126040, 126980
],
[
113800, 114750, 115700, 116650, 117600, 118550, 119500, 120450,
121400, 122350, 123300, 124250, 125200, 126150, 127100, 128050
],
[
114720, 115680, 116640, 117600, 118560, 119520, 120480, 121440,
122400, 123360, 124320, 125280, 126240, 127200, 128160, 129120
],
[
115640, 116610, 117580, 118550, 119520, 120490, 121460, 122430,
123400, 124370, 125340, 126310, 127280, 128250, 129220, 130190
],
[
116560, 117540, 118520, 119500, 120480, 121460, 122440, 123420,
124400, 125380, 126360, 127340, 128320, 129300, 130280, 131260
],
[
117480, 118470, 119460, 120450, 121440, 122430, 123420, 124410,
125400, 126390, 127380, 128370, 129360, 130350, 131340, 132330
],
[
118400, 119400, 120400, 121400, 122400, 123400, 124400, 125400,
126400, 127400, 128400, 129400, 130400, 131400, 132400, 133400
],
[
119320, 120330, 121340, 122350, 123360, 124370, 125380, 126390,
127400, 128410, 129420, 130430, 131440, 132450, 133460, 134470
],
[
120240, 121260, 122280, 123300, 124320, 125340, 126360, 127380,
128400, 129420, 130440, 131460, 132480, 133500, 134520, 135540
],
[
121160, 122190, 123220, 124250, 125280, 126310, 127340, 128370,
129400, 130430, 131460, 132490, 133520, 134550, 135580, 136610
],
[
122080, 123120, 124160, 125200, 126240, 127280, 128320, 129360,
130400, 131440, 132480, 133520, 134560, 135600, 136640, 137680
],
[
123000, 124050, 125100, 126150, 127200, 128250, 129300, 130350,
131400, 132450, 133500, 134550, 135600, 136650, 137700, 138750
],
[
123920, 124980, 126040, 127100, 128160, 129220, 130280, 131340,
132400, 133460, 134520, 135580, 136640, 137700, 138760, 139820
],
[
124840, 125910, 126980, 128050, 129120, 130190, 131260, 132330,
133400, 134470, 135540, 136610, 137680, 138750, 139820, 140890
],
[
125760, 126840, 127920, 129000, 130080, 131160, 132240, 133320,
134400, 135480, 136560, 137640, 138720, 139800, 140880, 141960
],
[
126680, 127770, 128860, 129950, 131040, 132130, 133220, 134310,
135400, 136490, 137580, 138670, 139760, 140850, 141940, 143030
]])
expected_result = 1 + np.diag(C)
actual_result = kernel._getMatrix(1, 1, A, B, only_diag=True)
testFunction(statement, expected_result, actual_result)