fixed (AE, VAE, NNMF)

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tods/detection_algorithm/PyodAE.py

@@ -56,7 +56,7 @@ class Hyperparams(Hyperparams_ODBase):
     ######## Add more Hyperparamters #######
 
     hidden_neurons = hyperparams.List(
-        default=[1, 2, 1],
+        default=[1, 4, 1],
         elements=hyperparams.Hyperparameter[int](1),
         description='The number of neurons per hidden layers.',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']

tods/detection_algorithm/PyodVAE.py

@@ -56,7 +56,7 @@ class Hyperparams(Hyperparams_ODBase):
     ######## Add more Hyperparamters #######
 
     encoder_neurons = hyperparams.List(
-        default=[1, 2, 1],
+        default=[1, 4, 1],
         elements=hyperparams.Hyperparameter[int](1),
         description='The number of neurons per hidden layers in encoder.',
         semantic_types=['https://metadata.datadrivendiscovery.org/types/TuningParameter']

tods/tods_skinterface/test/detection_algorithm/AutoEncoder_skitest.py

@@ -1,8 +1,10 @@
 import numpy as np
 from tods.tods_skinterface.primitiveSKI.detection_algorithm.AutoEncoder_skinterface import AutoEncoderSKI
 
-X_train = np.array([[3., 4., 8., 16, 18, 13., 22., 36., 59., 128, 62, 67, 78, 100]])
-X_test = np.array([[3., 4., 8.6, 13.4, 22.5, 17, 19.2, 36.1, 127, -23, 59.2]])
+X_train = np.array([[3., 4., 8.]])
+X_test = np.array([[3., 4., 8.6]])
+# X_train = np.array([[3., 4., 8., 16, 18, 13., 22., 36., 59., 128, 62, 67, 78, 100]])
+# X_test = np.array([[3., 4., 8.6, 13.4, 22.5, 17, 19.2, 36.1, 127, -23, 59.2]])
 
 transformer = AutoEncoderSKI()
 transformer.fit(X_train)

tods/tods_skinterface/test/feature_analysis/NonNegativeMatrixFactorization_skitest.py

@@ -1,7 +1,7 @@
 import numpy as np
 from tods.tods_skinterface.primitiveSKI.feature_analysis.NonNegativeMatrixFactorization_skinterface import NonNegativeMatrixFactorizationSKI
 
-X_test = np.array([[3., 4., 8.6, 13.4, 22.5, 17, 19.2, 36.1, 127, -23, 59.2]])
+X_test = np.array([[3., 4., 8.6, 13.4, 22.5, 17, 19.2, 36.1, 127, 23, 59.2]])
 
 transformer = NonNegativeMatrixFactorizationSKI()
 X_transform = transformer.produce(X_test)