def testBlkDiagMatrixCompat(self):
""" Check incompatible matrix raises exception. """
# Create a differently shaped matrix
x = BlkDiagMatrix.from_list(self.blk_a[1:-1])
# code should raise
with pytest.raises(RuntimeError):
_ = x + self.blk_a
def setUp(self):
self.blk_a = BlkDiagMatrix.from_list([
np.array([[-0.30656809, -0.34287864, -0.00854488, 0.5275285],
[-0.34287864, -0.19752432, 0.17833916, -0.22052178],
[-0.00854488, 0.17833916, -0.4125285, -0.30338836],
[0.5275285, -0.22052178, -0.30338836, 0.12254553]]),
np.array([[-0.08041961, -0.29729055, -0.3960436],
[-0.29729055, -0.56196307, 0.0607334],
[-0.3960436, 0.0607334, -0.09843568]]),
np.array([[-0.08041961, -0.29729055, -0.3960436],
[-0.29729055, -0.56196307, 0.0607334],
[-0.3960436, 0.0607334, -0.09843568]]),
np.array([[0.04041207, -0.05335218, -0.55965714],
[-0.05335218, -0.62303735, -0.26739795],
[-0.55965714, -0.26739795, 0.07961834]]),
np.array([[0.04041207, -0.05335218, -0.55965714],
[-0.05335218, -0.62303735, -0.26739795],
[-0.55965714, -0.26739795, 0.07961834]]),
np.array([[0.05209133, 0.17877923], [0.17877923, -0.43246999]]),
np.array([[0.05209133, 0.17877923], [0.17877923, -0.43246999]]),
np.array([[0.00296652, 0.32398467], [0.32398467, -0.12518209]]),
np.array([[0.00296652, 0.32398467], [0.32398467, -0.12518209]]),
np.array([[-0.06749134, 0.37452234], [0.37452234, 0.1789093]]),
np.array([[-0.06749134, 0.37452234], [0.37452234, 0.1789093]]),
np.array([[-0.13551364]]),
np.array([[-0.13551364]]),
np.array([[-0.18975111]]),
np.array([[-0.18975111]]),
np.array([[-0.22661312]]),
np.array([[-0.22661312]])
])
self.blk_b = BlkDiagMatrix.from_list([
np.array([[-0.27457111, -0.33770709, -0.09067737, 0.52007584],
[-0.33770709, -0.24677034, 0.20639731, -0.2078888],
[-0.09067737, 0.20639731, -0.36507922, -0.37742705],
[0.52007584, -0.2078888, -0.37742705, 0.30641696]]),
np.array([[-0.05219921, -0.23403317, -0.46057571],
[-0.23403317, -0.58669018, 0.02892691],
[-0.46057571, 0.02892691, -0.01291359]]),
np.array([[-0.05219921, -0.23403317, -0.46057571],
[-0.23403317, -0.58669018, 0.02892691],
[-0.46057571, 0.02892691, -0.01291359]]),
np.array([[0.04180323, 0.03254542, -0.56899236],
[0.03254542, -0.5865448, -0.33459247],
[-0.56899236, -0.33459247, 0.23441378]]),
np.array([[0.04180323, 0.03254542, -0.56899236],
[0.03254542, -0.5865448, -0.33459247],
[-0.56899236, -0.33459247, 0.23441378]]),
np.array([[0.02685116, 0.25494821], [0.25494821, -0.33546046]]),
np.array([[0.02685116, 0.25494821], [0.25494821, -0.33546046]]),
np.array([[-0.04041477, 0.36934457], [0.36934457, 0.0278976]]),
np.array([[-0.04041477, 0.36934457], [0.36934457, 0.0278976]]),
np.array([[-0.11861509, 0.37524325], [0.37524325, 0.38311618]]),
np.array([[-0.11861509, 0.37524325], [0.37524325, 0.38311618]]),
np.array([[-0.18418421]]),
np.array([[-0.18418421]]),
np.array([[-0.22672867]]),
np.array([[-0.22672867]]),
np.array([[-0.24393745]]),
np.array([[-0.24393745]])
])
self.blk_partition = [(4, 4), (3, 3), (3, 3), (3, 3), (3, 3), (2, 2),
(2, 2), (2, 2), (2, 2), (2, 2), (2, 2), (1, 1),
(1, 1), (1, 1), (1, 1), (1, 1), (1, 1)]
def testBlkDiagMatrixSolve(self):
result = np.array([[
-2108.56909528, -1599.04367287, 12983.7980645, 4907.54823872,
11596.60358098
],
[
-13261.71782311, 19064.68118668, -7058.51163424,
-23505.65277468, 10718.98836096
],
[
509.34615374, -6346.73823956, 12742.41838169,
26050.66900347, -11082.31307888
],
[
-3307.59390503, -2868.45197933, -9056.27254017,
-13121.74603406, 10435.30033164
],
[
5194.61155267, 4738.83799474, 1275.99271119,
5695.36574982, 317.72274105
],
[
-7192.22592633, -7706.51971094, -2706.50848048,
-5226.00224303, -10609.21894342
],
[
2106.79729152, 720.663365, 477.29928858,
2865.35110109, 2523.12141081
],
[
3293.89400066, 8296.32959773, 6274.69897306,
-6876.89810121, -5862.81117304
],
[
-5037.67048311, 4648.20122872, -6478.90991549,
5611.52398572, 5231.26195727
],
[
319.85059444, -13526.13244267, 5392.6728323,
-5960.86618271, -9989.22959649
],
[
-6810.8349795, -5587.85203171, -4557.56983224,
-21895.30335831, -14766.02390764
],
[
-1487.82495778, -17267.75186366, 2518.13873842,
16122.34779131, -10824.59882957
],
[
1696.60328706, 7524.33425367, -4412.35449613,
-8159.19336023, 3283.61842699
],
[
16533.21857068, 1048.77054126, 270.18096236,
3130.66029662, 3802.78276484
],
[
3987.29419815, 14592.92355119, -10535.19114199,
-11751.50898025, 10280.91948022
],
[
8221.61307636, 4963.62327863, 15482.47214868,
9215.02309094, 2026.10702896
],
[
10605.58662119, 4939.08507375, -6298.18788412,
2199.78159731, 6348.7733857
],
[
8939.66366506, 5979.70412996, 3984.91381065,
5566.93943721, -1421.24116192
],
[
-21606.51783477, 4822.39145187, 6685.88851131,
15766.49583437, -12707.40988432
],
[
-4230.44592724, 5420.21451676, -235.85955642,
18100.20123655, -4768.83642194
],
[
9450.93104008, 8900.03420762, 5202.48555337,
-201.77556792, 5466.57970732
],
[
11583.03440042, 75.37078542, 19981.80654578,
-358.04535766, -6139.4067836
],
[
12333.72389213, 1438.96251241, -8437.87301414,
17219.37822955, 11248.37259986
],
[
2193.30045686, 7010.7452714, 7716.40327545,
-7755.61096007, 4158.2657834
],
[
-16260.99699839, -24157.30011252, 8957.72261107,
7178.33063461, 8681.06123862
],
[
9062.40740653, 14709.69068774, 5430.20220187,
-6558.47236417, -7115.41480134
],
[
7468.93152378, -11727.69088497, 13044.41696129,
-1429.80060688, 615.43370603
],
[
5660.19210206, 758.33179619, 457.57835518,
8210.45409543, -6029.85682174
],
[
-11618.39974191, -4792.87662117, 7891.4438672,
-13769.59940549, 21193.75984068
],
[
-10062.76974695, -12184.86042594, 3139.30111484,
5233.94758207, 5060.12404909
],
[
20338.94250519, -5108.5476594, -7916.199259,
-13155.55120958, -2631.98451114
],
[
-8575.21386665, -3075.44542941, 6705.35386167,
1366.04418311, -3451.27892975
],
[
11471.10856558, -18990.29443441, -5960.30374281,
10147.12732058, 2454.46580692
],
[
23451.62021758, 9401.91573045, -5248.02166459,
-7872.80360867, 4596.97842653
]])
sn_matrix = [
np.array([[
1.29245300e-08, -6.49163277e-10, 2.45070400e-09, 1.53410891e-10
],
[
-6.49163277e-10, 1.20780198e-08, 3.75068433e-09,
-2.53453097e-09
],
[
2.45070400e-09, 3.75068433e-09, 1.07092249e-08,
1.63163421e-09
],
[
1.53410891e-10, -2.53453097e-09, 1.63163421e-09,
1.57226938e-08
]]),
np.array([[1.29101793e-08, 8.87468230e-09, 3.67875235e-09],
[8.87468230e-09, 5.05913176e-08, 2.61651494e-08],
[3.67875235e-09, 2.61651494e-08, 3.12014975e-08]]),
np.array([[1.29101793e-08, 8.87468230e-09, 3.67875235e-09],
[8.87468230e-09, 5.05913176e-08, 2.61651494e-08],
[3.67875235e-09, 2.61651494e-08, 3.12014975e-08]]),
np.array([[1.09084572e-08, 6.16238273e-12, -1.67985227e-09],
[6.16238273e-12, 9.71880814e-09, 6.56599364e-09],
[-1.67985227e-09, 6.56599364e-09, 1.98674901e-08]]),
np.array([[1.09084572e-08, 6.16238273e-12, -1.67985227e-09],
[6.16238273e-12, 9.71880814e-09, 6.56599364e-09],
[-1.67985227e-09, 6.56599364e-09, 1.98674901e-08]]),
np.array([[1.44374266e-08, -4.43138611e-09],
[-4.43138611e-09, 1.13592375e-08]]),
np.array([[1.44374266e-08, -4.43138611e-09],
[-4.43138611e-09, 1.13592375e-08]]),
np.array([[1.01359586e-08, 1.48988673e-09],
[1.48988673e-09, 8.98955124e-09]]),
np.array([[1.01359586e-08, 1.48988673e-09],
[1.48988673e-09, 8.98955124e-09]]),
np.array([[1.12133339e-08, 1.87797581e-09],
[1.87797581e-09, 9.22078294e-09]]),
np.array([[1.12133339e-08, 1.87797581e-09],
[1.87797581e-09, 9.22078294e-09]]),
np.array([[9.17068059e-09]]),
np.array([[9.17068059e-09]]),
np.array([[8.62262996e-09]]),
np.array([[8.62262996e-09]]),
np.array([[8.80222582e-09]]),
np.array([[8.80222582e-09]])
]
coeff = np.array(
[[
-1.79024086e-05, -4.90370074e-05, 2.02230179e-04,
1.40516221e-04, 1.17363697e-04
],
[
-1.48512889e-04, 2.14767206e-04, -2.29352560e-05,
-1.56122232e-04, 5.39212146e-05
],
[
-5.48500768e-05, -5.06209331e-06, 1.27030097e-04,
1.81437248e-04, -3.30330057e-05
],
[
-1.78844625e-05, -1.04020683e-04, -1.01716162e-04,
-1.03475356e-04, 1.20600188e-04
],
[
1.09850319e-05, -4.56252384e-06, -5.79024234e-06,
3.76900005e-05, -8.07696512e-05
],
[
-2.62638993e-04, -3.28971040e-04, -1.13113193e-04,
-1.38873438e-04, -4.67896828e-04
],
[
-1.03340746e-04, -1.61723452e-04, -5.12296850e-05,
-2.63840441e-05, -1.97697809e-04
],
[
-1.00631180e-06, 9.85991203e-05, 4.33475295e-05,
-6.09100454e-05, -6.60120574e-05
],
[
-2.17261186e-04, -4.51273621e-05, -1.30990539e-04,
6.68971521e-05, -4.87438362e-05
],
[
-1.09714163e-04, -2.69894566e-04, 2.18208856e-05,
-6.44599929e-05, -1.96370002e-04
],
[
-7.71549133e-05, -7.37010252e-05, -4.22884340e-05,
-2.25038388e-04, -1.66657239e-04
],
[
-3.36196989e-06, -1.18451671e-04, -4.52626978e-06,
1.02981866e-04, -8.37329754e-05
],
[
3.53793964e-05, 4.54964533e-05, -6.34722823e-05,
-1.94625852e-05, 1.89677483e-05
],
[
1.66565383e-04, 3.19224191e-06, -2.31259304e-05,
1.85983791e-05, 3.81422875e-05
],
[
9.28366905e-05, 1.74423406e-04, -7.30022809e-07,
-5.36855858e-05, 1.13245124e-04
], [0.00016175, 0.00019267, 0.00023797, 0.00010066, 0.00010137],
[
1.13502277e-04, 4.48093004e-05, -1.08588317e-04,
7.08992725e-06, 9.79580181e-05
],
[
5.45503135e-05, 4.60378864e-05, 7.31752847e-05,
5.34881056e-05, -4.42780821e-05
],
[
-2.93195776e-04, 4.56038593e-05, 9.75722094e-05,
1.47418646e-04, -1.62329742e-04
],
[
4.76921830e-05, 4.01996255e-05, -3.23069382e-05,
1.35737054e-04, 2.14109414e-06
],
[
1.13051655e-04, 9.03226722e-05, 8.25028066e-05,
-2.57863583e-06, 4.62620049e-05
],
[
1.18207098e-04, 1.39375924e-05, 1.87378588e-04,
-3.51928983e-06, -4.70459273e-05
],
[
1.28281884e-04, 2.50304808e-05, -7.40293647e-05,
1.62979923e-04, 1.20208384e-04
],
[
3.80926384e-05, 6.51673450e-05, 5.67955276e-05,
-4.40645390e-05, 5.41397444e-05
],
[
-1.65321007e-04, -2.43259429e-04, 1.10643723e-04,
6.81763658e-05, 8.39810614e-05
],
[
5.30247326e-05, 9.02680397e-05, 6.68931022e-05,
-4.69935188e-05, -4.93068724e-05
],
[
9.43813269e-05, -1.30082385e-04, 1.47130724e-04,
-6.13797435e-07, -4.42286161e-06
],
[
6.62178755e-05, -1.50319069e-05, 2.87163302e-05,
7.30216841e-05, -5.44442313e-05
],
[
-1.06548633e-04, -4.39539406e-05, 7.23699111e-05,
-1.26276598e-04, 1.94361202e-04
],
[
-9.22824472e-05, -1.11743463e-04, 2.87895278e-05,
4.79988615e-05, 4.64047814e-05
],
[
1.75375175e-04, -4.40491161e-05, -6.82584569e-05,
-1.13435450e-04, -2.26946285e-05
],
[
-7.39408960e-05, -2.65184279e-05, 5.78177851e-05,
1.17788935e-05, -2.97591011e-05
],
[
1.00971288e-04, -1.67156860e-04, -5.24639395e-05,
8.93173061e-05, 2.16047623e-05
],
[
2.06426457e-04, 8.27577854e-05, -4.61942718e-05,
-6.92981952e-05, 4.04636422e-05
]])
A = BlkDiagMatrix.from_list(sn_matrix)
coeff_est = A.solve(coeff)
self.allallfunc(result, coeff_est)