Make test_value a numpy array

Author: jessegrabowski

pymc_experimental/tests/test_marginal_model.py

@@ -483,7 +483,7 @@ def test_marginalized_hmm_with_one_emission():
     m.marginalize([chain])
 
     logp_fn = m.compile_logp()
-    test_value = [-1, 1, -1, 1]
+    test_value = np.array([-1, 1, -1, 1])
 
     expected_logp = pm.logp(pm.Normal.dist(0, 1e-1), np.zeros_like(test_value)).sum().eval()
     np.testing.assert_allclose(logp_fn({f"emission": test_value}), expected_logp)
@@ -501,10 +501,8 @@ def test_marginalized_hmm_with_many_emissions():
         m.marginalize([chain])
 
     logp_fn = m.compile_logp()
-    test_value = [-1, 1, -1, 1]
+    test_value = np.array([-1, 1, -1, 1])
 
-    expected_logp = pm.logp(pm.Normal.dist(0, 1e-1), np.zeros_like(test_value)).sum().eval()
+    e_logp = pm.logp(pm.Normal.dist(0, 1e-1), np.zeros_like(test_value)).sum() * 2
     test_point = {"emission_1": test_value, "emission_2": test_value * -1}
-
-    assert False
-    # np.testing.assert_allclose(logp_fn(test_point), expected_logp)
+    np.testing.assert_allclose(logp_fn(test_point), e_logp.eval())