Updated params and loss values to assert against
Updated params and loss values to assert against since there was a bug in the sigmoid cross entropy loss calculation. Once that bug was fixed, the test case also needed to be updated to assert against the right param and loss values.
@@ -36,11 +36,17 @@ class TestQSVMVariational(QiskitAquaTestCase):
self.testing_data = {'A': np.asarray([[3.83274304, 2.45044227]]),
'B': np.asarray([[3.89557489, 0.31415927]])}
- self.ref_opt_params = np.asarray([2.93868096, 0.76735399, 4.21845289, 4.28731786,
- -4.64804051, -4.0103384, 3.62083309, -3.1466139,
- 3.36741576, 0.07314989, -1.92529824, -1.31781337,
- 2.2547051, 7.29971351, 3.74421673, -3.74280352])
- self.ref_train_loss = 0.4999339230552529
+ #self.ref_opt_params = np.asarray([2.93868096, 0.76735399, 4.21845289, 4.28731786,
+ # -4.64804051, -4.0103384, 3.62083309, -3.1466139,
+ # 3.36741576, 0.07314989, -1.92529824, -1.31781337,
+ # 2.2547051, 7.29971351, 3.74421673, -3.74280352])
+ self.ref_opt_params = np.asarray([122.28551130, -129.47707191, 21.32710162,
+ 193.27391988, 113.35447991, 43.28777349,
+ 192.60743511, 69.52868185, -24.10727981,
+ 58.17954520, -5.98442782, 174.96107202,
+ -92.50977466, -31.09174807, 1.47603779, 27.14646432])
+ #self.ref_train_loss = 0.4999339230552529
+ self.ref_train_loss = 34.145266463
self.svm_input = SVMInput(self.training_data, self.testing_data)
