def testWeightedHatMatrix(self):
expectedWeightedHatMatrix = np.array([[ 0.93746979, 0.22625449, 0.05730702, -0.00315076, -0.02629974,
-0.03331944, -0.03243355, -0.02737749, -0.02003319, -0.01142021],
[ 0.22625449, 0.08788299, 0.05898899, 0.05159613, 0.05137251,
0.05410201, 0.05817441, 0.0628748 , 0.0678539 , 0.07293014],
[ 0.05730702, 0.05898899, 0.06478378, 0.07085639, 0.07671941,
0.08229333, 0.08758368, 0.09261537, 0.09741573, 0.10200995],
[-0.00315076, 0.05159613, 0.07085639, 0.08238505, 0.09090112,
0.09788464, 0.10395253, 0.10941096, 0.11443131, 0.11911848],
[-0.02629974, 0.05137251, 0.07671941, 0.09090112, 0.1008532 ,
0.10872302, 0.11539093, 0.12128539, 0.12664119, 0.13159879],
[-0.03331944, 0.05410201, 0.08229333, 0.09788464, 0.10872302,
0.11723346, 0.12440767, 0.13072689, 0.13645396, 0.14174555],
[-0.03243355, 0.05817441, 0.08758368, 0.10395253, 0.11539093,
0.12440767, 0.13203013, 0.13875766, 0.14486348, 0.15051078],
[-0.02737749, 0.0628748 , 0.09261537, 0.10941096, 0.12128539,
0.13072689, 0.13875766, 0.14587641, 0.15235719, 0.15836442],
[-0.02003319, 0.0678539 , 0.09741573, 0.11443131, 0.12664119,
0.13645396, 0.14486348, 0.15235719, 0.15920448, 0.16556802],
[-0.01142021, 0.07293014, 0.10200995, 0.11911848, 0.13159879,
0.14174555, 0.15051078, 0.15836442, 0.16556802, 0.17228071]])
linearModel = LinearModel(self.regressorList, self.regressorNames, self.covMatrixObserv2)
self.assertTrue(np.allclose(linearModel.hatMatrix(), expectedWeightedHatMatrix, rtol=1.0e-6, atol=1.e-08))
def testHatMatrix(self):
expectedHatMatrix = np.array([[ 0.21264822, 0.20869565, 0.19683794, 0.1770751 , 0.14940711,
0.11383399, 0.07035573, 0.01897233, -0.04031621, -0.10750988],
[ 0.20869565, 0.20488177, 0.19344012, 0.17437071, 0.14767353,
0.11334859, 0.07139588, 0.02181541, -0.03539283, -0.10022883],
[ 0.19683794, 0.19344012, 0.18324665, 0.16625754, 0.14247278,
0.11189238, 0.07451633, 0.03034464, -0.0206227 , -0.07838569],
[ 0.1770751 , 0.17437071, 0.16625754, 0.15273559, 0.13380487,
0.10946536, 0.07971708, 0.04456002, 0.00399418, -0.04198045],
[ 0.14940711, 0.14767353, 0.14247278, 0.13380487, 0.12166979,
0.10606754, 0.08699813, 0.06446155, 0.0384578 , 0.00898689],
[ 0.11383399, 0.11334859, 0.11189238, 0.10946536, 0.10606754,
0.10169891, 0.09635948, 0.09004923, 0.08276819, 0.07451633],
[ 0.07035573, 0.07139588, 0.07451633, 0.07971708, 0.08699813,
0.09635948, 0.10780112, 0.12132307, 0.13692532, 0.15460786],
[ 0.01897233, 0.02181541, 0.03034464, 0.04456002, 0.06446155,
0.09004923, 0.12132307, 0.15828306, 0.2009292 , 0.24926149],
[-0.04031621, -0.03539283, -0.0206227 , 0.00399418, 0.0384578 ,
0.08276819, 0.13692532, 0.2009292 , 0.27477983, 0.35847722],
[-0.10750988, -0.10022883, -0.07838569, -0.04198045, 0.00898689,
0.07451633, 0.15460786, 0.24926149, 0.35847722, 0.48225504]])
linearModel = LinearModel(self.regressorList, self.regressorNames)
self.assertTrue(np.allclose(linearModel.hatMatrix(), expectedHatMatrix, rtol=1.0e-6, atol=1.e-08))
def testWeightedHatMatrix(self):
expectedWeightedHatMatrix = np.array(
[
[
0.93746979,
0.22625449,
0.05730702,
-0.00315076,
-0.02629974,
-0.03331944,
-0.03243355,
-0.02737749,
-0.02003319,
-0.01142021,
],
[
0.22625449,
0.08788299,
0.05898899,
0.05159613,
0.05137251,
0.05410201,
0.05817441,
0.0628748,
0.0678539,
0.07293014,
],
[
0.05730702,
0.05898899,
0.06478378,
0.07085639,
0.07671941,
0.08229333,
0.08758368,
0.09261537,
0.09741573,
0.10200995,
],
[
-0.00315076,
0.05159613,
0.07085639,
0.08238505,
0.09090112,
0.09788464,
0.10395253,
0.10941096,
0.11443131,
0.11911848,
],
[
-0.02629974,
0.05137251,
0.07671941,
0.09090112,
0.1008532,
0.10872302,
0.11539093,
0.12128539,
0.12664119,
0.13159879,
],
[
-0.03331944,
0.05410201,
0.08229333,
0.09788464,
0.10872302,
0.11723346,
0.12440767,
0.13072689,
0.13645396,
0.14174555,
],
[
-0.03243355,
0.05817441,
0.08758368,
0.10395253,
0.11539093,
0.12440767,
0.13203013,
0.13875766,
0.14486348,
0.15051078,
],
[
-0.02737749,
0.0628748,
0.09261537,
0.10941096,
0.12128539,
0.13072689,
0.13875766,
0.14587641,
0.15235719,
0.15836442,
],
[
-0.02003319,
0.0678539,
0.09741573,
0.11443131,
0.12664119,
0.13645396,
0.14486348,
0.15235719,
0.15920448,
0.16556802,
],
[
-0.01142021,
0.07293014,
0.10200995,
0.11911848,
0.13159879,
0.14174555,
0.15051078,
0.15836442,
0.16556802,
0.17228071,
],
]
)
linearModel = LinearModel(self.regressorList, self.regressorNames, self.covMatrixObserv2)
self.assertTrue(np.allclose(linearModel.hatMatrix(), expectedWeightedHatMatrix, rtol=1.0e-6, atol=1.0e-08))
def testHatMatrix(self):
expectedHatMatrix = np.array(
[
[
0.21264822,
0.20869565,
0.19683794,
0.1770751,
0.14940711,
0.11383399,
0.07035573,
0.01897233,
-0.04031621,
-0.10750988,
],
[
0.20869565,
0.20488177,
0.19344012,
0.17437071,
0.14767353,
0.11334859,
0.07139588,
0.02181541,
-0.03539283,
-0.10022883,
],
[
0.19683794,
0.19344012,
0.18324665,
0.16625754,
0.14247278,
0.11189238,
0.07451633,
0.03034464,
-0.0206227,
-0.07838569,
],
[
0.1770751,
0.17437071,
0.16625754,
0.15273559,
0.13380487,
0.10946536,
0.07971708,
0.04456002,
0.00399418,
-0.04198045,
],
[
0.14940711,
0.14767353,
0.14247278,
0.13380487,
0.12166979,
0.10606754,
0.08699813,
0.06446155,
0.0384578,
0.00898689,
],
[
0.11383399,
0.11334859,
0.11189238,
0.10946536,
0.10606754,
0.10169891,
0.09635948,
0.09004923,
0.08276819,
0.07451633,
],
[
0.07035573,
0.07139588,
0.07451633,
0.07971708,
0.08699813,
0.09635948,
0.10780112,
0.12132307,
0.13692532,
0.15460786,
],
[
0.01897233,
0.02181541,
0.03034464,
0.04456002,
0.06446155,
0.09004923,
0.12132307,
0.15828306,
0.2009292,
0.24926149,
],
[
-0.04031621,
-0.03539283,
-0.0206227,
0.00399418,
0.0384578,
0.08276819,
0.13692532,
0.2009292,
0.27477983,
0.35847722,
],
[
-0.10750988,
-0.10022883,
-0.07838569,
-0.04198045,
0.00898689,
0.07451633,
0.15460786,
0.24926149,
0.35847722,
0.48225504,
],
]
)
linearModel = LinearModel(self.regressorList, self.regressorNames)
self.assertTrue(np.allclose(linearModel.hatMatrix(), expectedHatMatrix, rtol=1.0e-6, atol=1.0e-08))
