Failing test for #7

oseiskar · Sep 17, 2019 · 7e9e589 · 7e9e589
1 parent 139183f
commit 7e9e589
Showing 1 changed file with 45 additions and 0 deletions.
diff --git a/tests/testsuite.py b/tests/testsuite.py
@@ -357,6 +357,30 @@ def test_multi_dimensional_observations(self):
         self.assertSequenceEqual(r.predicted.observations.mean.shape, (5,15,2))
         self.assertSequenceEqual(r.predicted.observations.cov.shape, (5,15,2,2))
 
+    def test_4_dimensional_observations(self):
+        training_matrix = np.ones((5,10,4))
+        training_matrix[1,1,1] = np.nan
+
+        kf = simdkalman.KalmanFilter(
+            state_transition = np.eye(3),
+            process_noise = 0.1,
+            observation_model = np.array([[1,1,0],[0,0,1],[0,1,1],[1,1,0]]),
+            observation_noise = np.eye(4))
+
+        r = kf.compute(training_matrix, 15,
+            gains=True, filtered=True, smoothed=True, log_likelihood=True)
+
+        self.assertSequenceEqual(
+            r.smoothed.observations.mean.shape,
+            training_matrix.shape)
+
+        self.assertSequenceEqual(r.smoothed.states.mean.shape, (5,10,3))
+        self.assertSequenceEqual(r.smoothed.states.cov.shape, (5,10,3,3))
+        self.assertSequenceEqual(r.smoothed.gains.shape, (5,10,3,3))
+        self.assertSequenceEqual(r.filtered.gains.shape, (5,10,3,4))
+        self.assertSequenceEqual(r.predicted.observations.mean.shape, (5,15,4))
+        self.assertSequenceEqual(r.predicted.observations.cov.shape, (5,15,4,4))
+
     def test_em_algorithm(self):
         training_matrix = np.ones((5,10))
 
@@ -398,6 +422,27 @@ def test_em_algorithm_multi_dimensional_observations(self):
         self.assertSequenceEqual(B.shape, (5,2,2))
         self.assertTrue(min(np.linalg.eig(B[0,...])[0]) > 0)
 
+    def test_em_algorithm_3_dimensional_observations(self):
+        training_matrix = np.ones((5,10,3))
+        training_matrix[1,1,1] = np.nan
+
+        kf = simdkalman.KalmanFilter(
+            state_transition = np.eye(2),
+            process_noise = 0.1,
+            observation_model = np.array([[1,1], [0,1], [1,0]]),
+            observation_noise = 0.1)
+
+        r = kf.em(training_matrix, n_iter=5, verbose=False)
+
+        self.assertSequenceEqual(r.process_noise.shape, (5,2,2))
+        A0 = r.process_noise[0,...]
+        self.assertMatrixEqual(A0, A0.T, epsilon=1e-8)
+        self.assertTrue(min(np.linalg.eig(A0)[0]) > 0)
+
+        B = r.observation_noise
+        self.assertSequenceEqual(B.shape, (5,3,3))
+        self.assertTrue(min(np.linalg.eig(B[0,...])[0]) > 0)
+
     def test_one_dimensional(self):
 
         training_matrix = range(10)