diff --git a/dynamax/hidden_markov_model/models/test_models.py b/dynamax/hidden_markov_model/models/test_models.py
index f5810768..a653b09f 100644
--- a/dynamax/hidden_markov_model/models/test_models.py
+++ b/dynamax/hidden_markov_model/models/test_models.py
@@ -1,5 +1,4 @@
 import pytest
-from datetime import datetime
 import jax.numpy as jnp
 import jax.random as jr
 from jax import vmap
@@ -21,7 +20,7 @@
     (models.LowRankGaussianHMM, dict(num_states=4, emission_dim=3, emission_rank=1), None),
     (models.GaussianMixtureHMM, dict(num_states=4, num_components=2, emission_dim=3, emission_prior_mean_concentration=1.0), None),
     (models.DiagonalGaussianMixtureHMM, dict(num_states=4, num_components=2, emission_dim=3, emission_prior_mean_concentration=1.0), None),
-    (models.LinearRegressionHMM, dict(num_states=4, emission_dim=3, input_dim=5), jnp.ones((NUM_TIMESTEPS, 5))),
+    (models.LinearRegressionHMM, dict(num_states=3, emission_dim=3, input_dim=5), jr.normal(jr.PRNGKey(0),(NUM_TIMESTEPS, 5))),
     (models.LogisticRegressionHMM, dict(num_states=4, input_dim=5), jnp.ones((NUM_TIMESTEPS, 5))),
     (models.MultinomialHMM, dict(num_states=4, emission_dim=3, num_classes=5, num_trials=10), None),
     (models.PoissonHMM, dict(num_states=4, emission_dim=3), None),
@@ -31,7 +30,6 @@
 @pytest.mark.parametrize(["cls", "kwargs", "inputs"], CONFIGS)
 def test_sample_and_fit(cls, kwargs, inputs):
     hmm = cls(**kwargs)
-    #key1, key2 = jr.split(jr.PRNGKey(int(datetime.now().timestamp())))
     key1, key2 = jr.split(jr.PRNGKey(42))
     params, param_props = hmm.initialize(key1)
     states, emissions = hmm.sample(params, key2, num_timesteps=NUM_TIMESTEPS, inputs=inputs)