def test_conditional_mvn_diag_whiten(precomputed_cov_mats):
f_vals = torch.tensor(
[[-2.28405212, 0.92506561, 0.6771707, -0.24862164, 1.43093486],
[-0.4704259, -0.94033398, 0.39634775, -0.43562778, 0.27752191]],
dtype=utils.TORCH_FLOAT_TYPE)
f_qrt = torch.tensor(
[[[1.2913755, 0., 0., 0., 0.], [-0.21657062, 1.38901088, 0., 0., 0.],
[1.05706677, -1.69033564, 0.81725719, 0., 0.],
[0.24842882, -1.07745551, -1.31826034, 2.02463485, 0.],
[1.16116644, -0.09549434, -1.33062235, 0.84813075, 0.32257327]],
[[2.29744629, 0., 0., 0., 0.], [-0.37817459, 2.0557189, 0., 0., 0.],
[-1.14057908, -1.58899131, 2.05868389, 0., 0.],
[-1.02594764, -0.03201106, -0.20093787, 2.6618695, 0.],
[0.7124642, 0.47203433, 0.17535208, -1.18123066, 0.46893534]]],
dtype=utils.TORCH_FLOAT_TYPE)
f_dist = distributions.MultivariateNormal(f_vals, scale_tril=f_qrt)
cov_aa, cov_ba, cov_bb = precomputed_cov_mats
cov_aa = torch.diag(cov_aa)
mean, var = conditionals.conditional_gaussian(cov_aa,
cov_ba,
cov_bb,
f_dist,
return_full_cov_flag=False,
whiten=True)
expected_mean = np.array([[
0.9983176021, -3.9736127037, 0.6906933359, 2.0855503170, 0.0715756755,
-1.2503840376, -1.8458709690, 0.8542689689
],
[
-1.1424803951, -1.3786447703, -0.9203628273,
-2.2190894546, -0.1230040081, -1.4342874653,
-1.0501022539, -1.1778143477
]])
expected_var = np.array([[
14.2586345250, 14.0537436795, 9.2947300720, 14.1982420873,
7.1713542879, 19.1857305446, 18.6136875294, 10.9176187167
],
[
14.4353048611, 25.0036833948, 11.4511969440,
27.2123889213, 7.2121129353, 27.2155613383,
22.9932268895, 14.4766175341
]])
np.testing.assert_array_almost_equal(mean.numpy(), expected_mean)
np.testing.assert_array_almost_equal(var.numpy(), expected_var)
def test_conditional_mvn_diag(precomputed_cov_mats):
f_vals = torch.tensor(
[[-2.28405212, 0.92506561, 0.6771707, -0.24862164, 1.43093486],
[-0.4704259, -0.94033398, 0.39634775, -0.43562778, 0.27752191]],
dtype=utils.TORCH_FLOAT_TYPE)
f_qrt = torch.tensor(
[[[1.2913755, 0., 0., 0., 0.], [-0.21657062, 1.38901088, 0., 0., 0.],
[1.05706677, -1.69033564, 0.81725719, 0., 0.],
[0.24842882, -1.07745551, -1.31826034, 2.02463485, 0.],
[1.16116644, -0.09549434, -1.33062235, 0.84813075, 0.32257327]],
[[2.29744629, 0., 0., 0., 0.], [-0.37817459, 2.0557189, 0., 0., 0.],
[-1.14057908, -1.58899131, 2.05868389, 0., 0.],
[-1.02594764, -0.03201106, -0.20093787, 2.6618695, 0.],
[0.7124642, 0.47203433, 0.17535208, -1.18123066, 0.46893534]]],
dtype=utils.TORCH_FLOAT_TYPE)
f_dist = distributions.MultivariateNormal(f_vals, scale_tril=f_qrt)
cov_aa, cov_ba, cov_bb = precomputed_cov_mats
cov_aa = torch.diag(cov_aa)
mean, var = conditionals.conditional_gaussian(cov_aa,
cov_ba,
cov_bb,
f_dist,
return_full_cov_flag=False,
whiten=False)
expected_mean = np.array([[
1.0395413577, -1.8514740569, 0.3141941933, 0.8476137584, 0.0382751075,
-0.3854980598, -0.8345681935, 0.3999459519
],
[
-0.0090321213, -0.7372578999, -0.4162285925,
-0.8602622449, -0.0616100437, -0.4430488838,
-0.2661613907, -0.5424388696
]])
expected_var = np.array([[
5.5969765530, 6.3781133317, 6.9028852306, 3.7262482077, 7.1214550454,
7.0502994187, 7.7957730662, 6.8558710306
],
[
5.6527201408, 12.4449391102, 7.3493964930,
5.7165059115, 7.1316647669, 10.6323246135,
10.7472708960, 7.6216942496
]])
np.testing.assert_array_almost_equal(mean.numpy(), expected_mean)
np.testing.assert_array_almost_equal(var.numpy(), expected_var)
def test_conditional_delta_diag(precomputed_cov_mats):
f_vals = torch.tensor(
[[1.23, -0.23, 7.45, 2.45, -3.45], [0.85, 6.4, -8.21, -0.45, -0.7]],
dtype=utils.TORCH_FLOAT_TYPE)
f_vals = custom_distributions.Delta(f_vals)
cov_aa, cov_ba, cov_bb = precomputed_cov_mats
cov_aa = torch.diag(cov_aa)
mean, var = conditionals.conditional_gaussian(cov_aa,
cov_ba,
cov_bb,
f_vals,
return_full_cov_flag=False,
whiten=False)
expected_mean = np.array([[
1.6243021303, 3.9762279529, -1.0018448174, -0.8217521943,
-0.2007619034, 3.9222665183, 5.0673831602, -1.4141233969
],
[
-1.6721866760, 0.6966202877, 3.5202238201,
6.3271136175, 0.5595452083, -0.9492091724,
-2.9775444011, 4.6694232859
]])
expected_var = np.array([[
4.4402744540, 3.1750354618, 6.2603802337, 1.7130353089, 7.1050700747,
3.4529913441, 3.8891409966, 5.7185561873
],
[
4.4402744540, 3.1750354618, 6.2603802337,
1.7130353089, 7.1050700747, 3.4529913441,
3.8891409966, 5.7185561873
]])
np.testing.assert_array_almost_equal(mean.numpy(), expected_mean)
np.testing.assert_array_almost_equal(var.numpy(), expected_var)
def test_conditional_delta_diag_whiten(precomputed_cov_mats):
f_vals = torch.tensor(
[[1.23, -0.23, 7.45, 2.45, -3.45], [0.85, 6.4, -8.21, -0.45, -0.7]],
dtype=utils.TORCH_FLOAT_TYPE)
f_vals = custom_distributions.Delta(f_vals)
cov_aa, cov_ba, cov_bb = precomputed_cov_mats
cov_aa = torch.diag(cov_aa)
mean, var = conditionals.conditional_gaussian(cov_aa,
cov_ba,
cov_bb,
f_vals,
return_full_cov_flag=False,
whiten=True)
expected_mean = np.array([[
6.6308755724, 7.3815405044, -2.0869767152, -1.6895302877,
-0.4139590835, 9.1604886668, 10.8441638657, -2.9321998024
],
[
0.2575228726, 0.6755548289, 7.6874194130,
15.9547405928, 1.1266581340, -0.2008611798,
-3.7909059985, 10.0338506274
]])
expected_var = np.array([[
4.4402744540, 3.1750354618, 6.2603802337, 1.7130353089, 7.1050700747,
3.4529913441, 3.8891409966, 5.7185561873
],
[
4.4402744540, 3.1750354618, 6.2603802337,
1.7130353089, 7.1050700747, 3.4529913441,
3.8891409966, 5.7185561873
]])
np.testing.assert_array_almost_equal(mean.numpy(), expected_mean)
np.testing.assert_array_almost_equal(var.numpy(), expected_var)
def test_conditional_mvn_full_cov_whiten(precomputed_cov_mats):
f_vals = torch.tensor(
[[-2.28405212, 0.92506561, 0.6771707, -0.24862164, 1.43093486],
[-0.4704259, -0.94033398, 0.39634775, -0.43562778, 0.27752191]],
dtype=utils.TORCH_FLOAT_TYPE)
f_qrt = torch.tensor(
[[[1.2913755, 0., 0., 0., 0.], [-0.21657062, 1.38901088, 0., 0., 0.],
[1.05706677, -1.69033564, 0.81725719, 0., 0.],
[0.24842882, -1.07745551, -1.31826034, 2.02463485, 0.],
[1.16116644, -0.09549434, -1.33062235, 0.84813075, 0.32257327]],
[[2.29744629, 0., 0., 0., 0.], [-0.37817459, 2.0557189, 0., 0., 0.],
[-1.14057908, -1.58899131, 2.05868389, 0., 0.],
[-1.02594764, -0.03201106, -0.20093787, 2.6618695, 0.],
[0.7124642, 0.47203433, 0.17535208, -1.18123066, 0.46893534]]],
dtype=utils.TORCH_FLOAT_TYPE)
f_dist = distributions.MultivariateNormal(f_vals, scale_tril=f_qrt)
cov_aa, cov_ba, cov_bb = precomputed_cov_mats
mean, var = conditionals.conditional_gaussian(cov_aa,
cov_ba,
cov_bb,
f_dist,
return_full_cov_flag=True,
whiten=True)
expected_mean = np.array([[
0.9983176021, -3.9736127037, 0.6906933359, 2.0855503170, 0.0715756755,
-1.2503840376, -1.8458709690, 0.8542689689
],
[
-1.1424803951, -1.3786447703, -0.9203628273,
-2.2190894546, -0.1230040081, -1.4342874653,
-1.0501022539, -1.1778143477
]])
expected_var = np.array(
[[[
14.2586345250, 9.1822888138, -2.6004046698, -4.0531016056,
-0.3628278468, 12.8795825333, 11.8793483846, -3.3109476844
],
[
9.1822888138, 14.0537436795, -3.0078651759, -5.9977582226,
-0.4290556306, 12.5841457955, 11.9920397559, -3.9239952650
],
[
-2.6004046698, -3.0078651759, 9.2947300720, 7.1303199998,
1.1779780651, -3.5088886346, -4.5304743531, 6.4427563661
],
[
-4.0531016056, -5.9977582226, 7.1303199998, 14.1982420873,
0.9852830967, -6.9005426267, -9.0417278718, 8.8555650766
],
[
-0.3628278468, -0.4290556306, 1.1779780651, 0.9852830967,
7.1713542879, -0.5043240844, -0.6476378715, 2.8130058040
],
[
12.8795825333, 12.5841457955, -3.5088886346, -6.9005426267,
-0.5043240844, 19.1857305446, 15.9460640932, -4.7058678889
],
[
11.8793483846, 11.9920397559, -4.5304743531, -9.0417278718,
-0.6476378715, 15.9460640932, 18.6136875294, -5.9171923298
],
[
-3.3109476844, -3.9239952650, 6.4427563661, 8.8555650766,
2.8130058040, -4.7058678889, -5.9171923298, 10.9176187167
]],
[[
14.4353048611, 7.6440115383, 1.0779014652, 5.6157728594,
0.0847217439, 14.3465260481, 11.8000939369, 1.3130612969
],
[
7.6440115383, 25.0036833948, -1.0122776158, -2.6434071073,
-0.0927873657, 19.4050847421, 17.3934295022, -1.2793426390
],
[
1.0779014652, -1.0122776158, 11.4511969440, 12.2886570287,
1.4698466940, -0.2455049374, -2.5919774292, 9.2114657874
],
[
5.6157728594, -2.6434071073, 12.2886570287, 27.2123889213,
1.6542042466, 1.3498022098, -4.1299704160, 15.4245118703
],
[
0.0847217439, -0.0927873657, 1.4698466940, 1.6542042466,
7.2121129353, -0.0816546991, -0.3961193855, 3.1899994546
],
[
14.3465260481, 19.4050847421, -0.2455049374, 1.3498022098,
-0.0816546991, 27.2155613383, 21.7753020846, -0.5858162823
],
[
11.8000939369, 17.3934295022, -2.5919774292, -4.1299704160,
-0.3961193855, 21.7753020846, 22.9932268895, -3.4791494930
],
[
1.3130612969, -1.2793426390, 9.2114657874, 15.4245118703,
3.1899994546, -0.5858162823, -3.4791494930, 14.4766175341
]]])
np.testing.assert_array_almost_equal(mean.numpy(), expected_mean)
np.testing.assert_array_almost_equal(var.numpy(), expected_var)
def test_conditional_mvn_full_cov(precomputed_cov_mats):
f_vals = torch.tensor(
[[-2.28405212, 0.92506561, 0.6771707, -0.24862164, 1.43093486],
[-0.4704259, -0.94033398, 0.39634775, -0.43562778, 0.27752191]],
dtype=utils.TORCH_FLOAT_TYPE)
f_qrt = torch.tensor(
[[[1.2913755, 0., 0., 0., 0.], [-0.21657062, 1.38901088, 0., 0., 0.],
[1.05706677, -1.69033564, 0.81725719, 0., 0.],
[0.24842882, -1.07745551, -1.31826034, 2.02463485, 0.],
[1.16116644, -0.09549434, -1.33062235, 0.84813075, 0.32257327]],
[[2.29744629, 0., 0., 0., 0.], [-0.37817459, 2.0557189, 0., 0., 0.],
[-1.14057908, -1.58899131, 2.05868389, 0., 0.],
[-1.02594764, -0.03201106, -0.20093787, 2.6618695, 0.],
[0.7124642, 0.47203433, 0.17535208, -1.18123066, 0.46893534]]],
dtype=utils.TORCH_FLOAT_TYPE)
f_dist = distributions.MultivariateNormal(f_vals, scale_tril=f_qrt)
cov_aa, cov_ba, cov_bb = precomputed_cov_mats
mean, var = conditionals.conditional_gaussian(cov_aa,
cov_ba,
cov_bb,
f_dist,
return_full_cov_flag=True,
whiten=False)
expected_mean = np.array([[
1.0395413577, -1.8514740569, 0.3141941933, 0.8476137584, 0.0382751075,
-0.3854980598, -0.8345681935, 0.3999459519
],
[
-0.0090321213, -0.7372578999, -0.4162285925,
-0.8602622449, -0.0616100437, -0.4430488838,
-0.2661613907, -0.5424388696
]])
expected_var = np.array(
[[[
5.5969765530, 0.8987337154, -1.0047342826, -0.9199924557,
-0.1427258369, 2.6960902126, 2.6752037660, -1.2360837610
],
[
0.8987337154, 6.3781133317, -0.6323356576, -1.1976265383,
-0.0958950157, 2.9462344884, 2.9279693862, -0.8347931889
],
[
-1.0047342826, -0.6323356576, 6.9028852306, 2.1426581153,
0.8332082246, -0.9641806581, -1.1681775127, 3.3260099106
],
[
-0.9199924557, -1.1976265383, 2.1426581153, 3.7262482077,
0.2696427010, -1.6835020270, -2.2145982904, 2.3613305103
],
[
-0.1427258369, -0.0958950157, 0.8332082246, 0.2696427010,
7.1214550454, -0.1550713938, -0.1714103286, 2.3634627316
],
[
2.6960902126, 2.9462344884, -0.9641806581, -1.6835020270,
-0.1550713938, 7.0502994187, 4.8934529747, -1.4055364028
],
[
2.6752037660, 2.9279693862, -1.1681775127, -2.2145982904,
-0.1714103286, 4.8934529747, 7.7957730662, -1.5401611640
],
[
-1.2360837610, -0.8347931889, 3.3260099106, 2.3613305103,
2.3634627316, -1.4055364028, -1.5401611640, 6.8558710306
]],
[[
5.6527201408, 0.2022671057, -0.6651704627, 0.0611261029,
-0.1054391073, 2.6737827700, 2.4564956142, -0.8162208570
],
[
0.2022671057, 12.4449391102, -0.2266653547, -0.4712621969,
-0.0265284815, 7.1430618936, 6.7646918858, -0.3103502309
],
[
-0.6651704627, -0.2266653547, 7.3493964930, 3.0578181632,
0.9001054998, -0.5528150962, -1.0520779708, 3.9105112597
],
[
0.0611261029, -0.4712621969, 3.0578181632, 5.7165059115,
0.4021351181, -0.6077735410, -1.7997565173, 3.5505399050
],
[
-0.1054391073, -0.0265284815, 0.9001054998, 0.4021351181,
7.1316647669, -0.1003523807, -0.1585082426, 2.4513695028
],
[
2.6737827700, 7.1430618936, -0.5528150962, -0.6077735410,
-0.1003523807, 10.6323246135, 8.0648547968, -0.8970605421
],
[
2.4564956142, 6.7646918858, -1.0520779708, -1.7997565173,
-0.1585082426, 8.0648547968, 10.7472708960, -1.4129403011
],
[
-0.8162208570, -0.3103502309, 3.9105112597, 3.5505399050,
2.4513695028, -0.8970605421, -1.4129403011, 7.6216942496
]]])
np.testing.assert_array_almost_equal(mean.numpy(), expected_mean)
np.testing.assert_array_almost_equal(var.numpy(), expected_var)
def test_conditional_delta_full_cov_whiten(precomputed_cov_mats):
f_vals = torch.tensor(
[[1.23, -0.23, 7.45, 2.45, -3.45], [0.85, 6.4, -8.21, -0.45, -0.7]],
dtype=utils.TORCH_FLOAT_TYPE)
f_vals = custom_distributions.Delta(f_vals)
cov_aa, cov_ba, cov_bb = precomputed_cov_mats
mean, var = conditionals.conditional_gaussian(cov_aa,
cov_ba,
cov_bb,
f_vals,
return_full_cov_flag=True,
whiten=True)
expected_mean = np.array([[
6.6308755724, 7.3815405044, -2.0869767152, -1.6895302877,
-0.4139590835, 9.1604886668, 10.8441638657, -2.9321998024
],
[
0.2575228726, 0.6755548289, 7.6874194130,
15.9547405928, 1.1266581340, -0.2008611798,
-3.7909059985, 10.0338506274
]])
expected_var = np.array(
[[[
4.4402744540e+00, -8.8794111845e-01, -5.2494440661e-01,
-6.7909241880e-02, -6.9165843075e-02, 7.7879338152e-01,
6.8652224581e-01, -5.9944796542e-01
],
[
-8.8794111845e-01, 3.1750354618e+00, 4.0894705634e-02,
3.9688953050e-02, 4.1088182004e-03, -4.3666741391e-01,
-4.0177948193e-01, 5.5950707268e-02
],
[
-5.2494440661e-01, 4.0894705634e-02, 6.2603802337e+00,
1.0111468375e+00, 7.3081832061e-01, -1.7098283477e-01,
-6.0924748295e-03, 2.4712302817e+00
],
[
-6.7909241880e-02, 3.9688953050e-02, 1.0111468375e+00,
1.7130353089e+00, 9.0428140734e-02, -2.2654425360e-01,
-1.4162076285e-01, 8.5758006162e-01
],
[
-6.9165843075e-02, 4.1088182004e-03, 7.3081832061e-01,
9.0428140734e-02, 7.1050700747e+00, -3.6494969401e-02,
8.4467531841e-03, 2.2271565406e+00
],
[
7.7879338152e-01, -4.3666741391e-01, -1.7098283477e-01,
-2.2654425360e-01, -3.6494969401e-02, 3.4529913441e+00,
1.3024795901e+00, -3.5594698486e-01
],
[
6.8652224581e-01, -4.0177948193e-01, -6.0924748295e-03,
-1.4162076285e-01, 8.4467531841e-03, 1.3024795901e+00,
3.8891409966e+00, 2.9443812570e-03
],
[
-5.9944796542e-01, 5.5950707268e-02, 2.4712302817e+00,
8.5758006162e-01, 2.2271565406e+00, -3.5594698486e-01,
2.9443812570e-03, 5.7185561873e+00
]],
[[
4.4402744540e+00, -8.8794111845e-01, -5.2494440661e-01,
-6.7909241880e-02, -6.9165843075e-02, 7.7879338152e-01,
6.8652224581e-01, -5.9944796542e-01
],
[
-8.8794111845e-01, 3.1750354618e+00, 4.0894705634e-02,
3.9688953050e-02, 4.1088182004e-03, -4.3666741391e-01,
-4.0177948193e-01, 5.5950707268e-02
],
[
-5.2494440661e-01, 4.0894705634e-02, 6.2603802337e+00,
1.0111468375e+00, 7.3081832061e-01, -1.7098283477e-01,
-6.0924748295e-03, 2.4712302817e+00
],
[
-6.7909241880e-02, 3.9688953050e-02, 1.0111468375e+00,
1.7130353089e+00, 9.0428140734e-02, -2.2654425360e-01,
-1.4162076285e-01, 8.5758006162e-01
],
[
-6.9165843075e-02, 4.1088182004e-03, 7.3081832061e-01,
9.0428140734e-02, 7.1050700747e+00, -3.6494969401e-02,
8.4467531841e-03, 2.2271565406e+00
],
[
7.7879338152e-01, -4.3666741391e-01, -1.7098283477e-01,
-2.2654425360e-01, -3.6494969401e-02, 3.4529913441e+00,
1.3024795901e+00, -3.5594698486e-01
],
[
6.8652224581e-01, -4.0177948193e-01, -6.0924748295e-03,
-1.4162076285e-01, 8.4467531841e-03, 1.3024795901e+00,
3.8891409966e+00, 2.9443812570e-03
],
[
-5.9944796542e-01, 5.5950707268e-02, 2.4712302817e+00,
8.5758006162e-01, 2.2271565406e+00, -3.5594698486e-01,
2.9443812570e-03, 5.7185561873e+00
]]])
np.testing.assert_array_almost_equal(mean.numpy(), expected_mean)
np.testing.assert_array_almost_equal(var.numpy(), expected_var)
def test_conditional_delta_full_cov(precomputed_cov_mats):
f_vals = torch.tensor(
[[1.23, -0.23, 7.45, 2.45, -3.45], [0.85, 6.4, -8.21, -0.45, -0.7]],
dtype=utils.TORCH_FLOAT_TYPE)
f_vals = custom_distributions.Delta(f_vals)
cov_aa, cov_ba, cov_bb = precomputed_cov_mats
mean, var = conditionals.conditional_gaussian(cov_aa,
cov_ba,
cov_bb,
f_vals,
return_full_cov_flag=True,
whiten=False)
expected_mean = np.array([[
1.6243021303, 3.9762279529, -1.0018448174, -0.8217521943,
-0.2007619034, 3.9222665183, 5.0673831602, -1.4141233969
],
[
-1.6721866760, 0.6966202877, 3.5202238201,
6.3271136175, 0.5595452083, -0.9492091724,
-2.9775444011, 4.6694232859
]])
expected_var = np.array(
[[[
4.4402744540e+00, -8.8794111845e-01, -5.2494440661e-01,
-6.7909241880e-02, -6.9165843075e-02, 7.7879338152e-01,
6.8652224581e-01, -5.9944796542e-01
],
[
-8.8794111845e-01, 3.1750354618e+00, 4.0894705634e-02,
3.9688953050e-02, 4.1088182004e-03, -4.3666741391e-01,
-4.0177948193e-01, 5.5950707268e-02
],
[
-5.2494440661e-01, 4.0894705634e-02, 6.2603802337e+00,
1.0111468375e+00, 7.3081832061e-01, -1.7098283477e-01,
-6.0924748295e-03, 2.4712302817e+00
],
[
-6.7909241880e-02, 3.9688953050e-02, 1.0111468375e+00,
1.7130353089e+00, 9.0428140734e-02, -2.2654425360e-01,
-1.4162076285e-01, 8.5758006162e-01
],
[
-6.9165843075e-02, 4.1088182004e-03, 7.3081832061e-01,
9.0428140734e-02, 7.1050700747e+00, -3.6494969401e-02,
8.4467531841e-03, 2.2271565406e+00
],
[
7.7879338152e-01, -4.3666741391e-01, -1.7098283477e-01,
-2.2654425360e-01, -3.6494969401e-02, 3.4529913441e+00,
1.3024795901e+00, -3.5594698486e-01
],
[
6.8652224581e-01, -4.0177948193e-01, -6.0924748295e-03,
-1.4162076285e-01, 8.4467531841e-03, 1.3024795901e+00,
3.8891409966e+00, 2.9443812570e-03
],
[
-5.9944796542e-01, 5.5950707268e-02, 2.4712302817e+00,
8.5758006162e-01, 2.2271565406e+00, -3.5594698486e-01,
2.9443812570e-03, 5.7185561873e+00
]],
[[
4.4402744540e+00, -8.8794111845e-01, -5.2494440661e-01,
-6.7909241880e-02, -6.9165843075e-02, 7.7879338152e-01,
6.8652224581e-01, -5.9944796542e-01
],
[
-8.8794111845e-01, 3.1750354618e+00, 4.0894705634e-02,
3.9688953050e-02, 4.1088182004e-03, -4.3666741391e-01,
-4.0177948193e-01, 5.5950707268e-02
],
[
-5.2494440661e-01, 4.0894705634e-02, 6.2603802337e+00,
1.0111468375e+00, 7.3081832061e-01, -1.7098283477e-01,
-6.0924748295e-03, 2.4712302817e+00
],
[
-6.7909241880e-02, 3.9688953050e-02, 1.0111468375e+00,
1.7130353089e+00, 9.0428140734e-02, -2.2654425360e-01,
-1.4162076285e-01, 8.5758006162e-01
],
[
-6.9165843075e-02, 4.1088182004e-03, 7.3081832061e-01,
9.0428140734e-02, 7.1050700747e+00, -3.6494969401e-02,
8.4467531841e-03, 2.2271565406e+00
],
[
7.7879338152e-01, -4.3666741391e-01, -1.7098283477e-01,
-2.2654425360e-01, -3.6494969401e-02, 3.4529913441e+00,
1.3024795901e+00, -3.5594698486e-01
],
[
6.8652224581e-01, -4.0177948193e-01, -6.0924748295e-03,
-1.4162076285e-01, 8.4467531841e-03, 1.3024795901e+00,
3.8891409966e+00, 2.9443812570e-03
],
[
-5.9944796542e-01, 5.5950707268e-02, 2.4712302817e+00,
8.5758006162e-01, 2.2271565406e+00, -3.5594698486e-01,
2.9443812570e-03, 5.7185561873e+00
]]])
np.testing.assert_array_almost_equal(mean.numpy(), expected_mean)
np.testing.assert_array_almost_equal(var.numpy(), expected_var)