@@ -19,6 +19,7 @@ def syn_data_3d(self):
1919 # generate transition matrix, time lag 2
2020 np .random .seed (0 )
2121 A = 0.2 * np .random .rand (3 ,6 )
22+ print ('True matrix is \n {}' .format (A ))
2223 # generate time series
2324 T = 1000
2425 data = np .random .rand (3 , T )
@@ -34,6 +35,7 @@ def syn_data_2d(self):
3435 A = 0.5 * np .random .rand (2 ,4 )
3536 A [0 ,1 ] = 0
3637 A [0 ,3 ] = 0
38+ print ('True matrix is \n {}' .format (A ))
3739 # generate time series
3840 T = 100
3941 data = np .random .rand (2 , T )
@@ -52,10 +54,8 @@ def test_granger_test(self):
5254 dataset = self .syn_data_2d ()
5355 G = Granger ()
5456 p_value_matrix , adj_matrix = G .granger_test_2d (data = dataset )
55- p_value_matrix_truth = np .array ([[0 , 0.5989 , 0 , 0.5397 ], [0.0006 , 0 , 0.0014 , 0 ]])
56- adj_matrix_truth = np .array ([[1 , 0 , 1 , 0 ], [1 , 1 , 1 , 1 ]])
57- self .assertEqual ((np .round (p_value_matrix , 4 ) - p_value_matrix_truth ).all (), 0 )
58- self .assertEqual ((adj_matrix - adj_matrix_truth ).all (), 0 )
57+ print ('P-value matrix is \n {}' .format (p_value_matrix ))
58+ print ('Adjacency matrix is \n {}' .format (adj_matrix ))
5959
6060 # example2
6161 # for data with multi-dimensional variables, granger lasso regression.
@@ -66,10 +66,7 @@ def test_granger_lasso(self):
6666 dataset = self .syn_data_3d ()
6767 G = Granger ()
6868 coeff = G .granger_lasso (data = dataset )
69- coeff_truth = np .array ([[0.09 , 0.1101 , 0.1527 , 0.1127 , 0.0226 , 0.1538 ],
70- [0.1004 , 0.15 , 0.1757 , 0.1037 , 0.1612 , 0.0987 ],
71- [0.1155 , 0.1485 , 0 , 0.039 , - 0. , 0.1085 ]])
72- self .assertEqual ((np .round (coeff , 4 ) - coeff_truth ).all (), 0 )
69+ print ('Estimated matrix is \n {}' .format (coeff ))
7370
7471
7572if __name__ == '__main__' :
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