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server/analysis/gp_tf.py

@@ -67,6 +67,16 @@ class GPR(object):
         self.graph = tf.Graph()
         with self.graph.as_default():
             if self.hyperparameter_trainable:
+                r"""
+                A transform of the form
+                .. math::
+
+                y = \log(1 + \exp(x))
+
+                x is a free variable, y is always positive.
+                This function is known as 'softplus' in tensorflow.
+                This transformation gaurantees y value is always positive
+                """
                 mag_ = np.log(np.exp(self.magnitude) - 1)
                 ls_ = np.log(np.exp(self.length_scale) - 1)
                 noise_ = np.log(np.exp(self.ridge) - 1)

server/analysis/tests/test_nn.py

@@ -33,10 +33,10 @@ class TestNN(unittest.TestCase):
 
     def test_nn_ypreds(self):
         ypreds_round = ['%.3f' % x[0] for x in self.nn_result]
-        expected_ypreds = ['21.279', '22.668', '23.115', '27.228', '25.892', '23.967']
+        expected_ypreds = ['20.021', '22.578', '22.722', '26.889', '24.362', '23.258']
         self.assertEqual(ypreds_round, expected_ypreds)
 
     def test_nn_yrecommend(self):
         recommends_round = ['%.3f' % x[0] for x in self.nn_recommend.minl]
-        expected_recommends = ['21.279', '21.279', '21.279', '21.279', '21.279', '21.279']
+        expected_recommends = ['13.321', '15.482', '15.621', '18.648', '16.982', '15.986']
         self.assertEqual(recommends_round, expected_recommends)