def test_random_means_cluster_init(self):
"""
Test state estimation with random means and weights.
20 cells, 200 genes, 2 clusters
"""
sim_m, sim_w = simulation.generate_poisson_states(2, 20, 200)
sim_data = simulation.generate_state_data(sim_m, sim_w)
sim_data = sparse.csc_matrix(sim_data)
m, w, ll = state_estimation.poisson_estimate_state(
sim_data, 2, max_iters=10, disp=False, initialization='cluster')
obj = sparse_objective(sim_data.data, sim_data.indices,
sim_data.indptr, 20, 200, m, w)
self.assertEqual(ll, obj)
dense_obj = objective(sim_data.toarray(), m, w)
self.assertTrue(np.abs(obj - dense_obj) < 1e-6)
means_good = False
weights_good = False
for p in itertools.permutations([0, 1]):
means_good = means_good or (np.mean(np.abs(sim_m - m[:, p])) <
20.0)
weights_good = weights_good or (np.mean(np.abs(sim_w - w[p, :])) <
0.2)
self.assertTrue(means_good)
self.assertTrue(weights_good)
def _call_sparse_obj(X, M, W):
return sparse_objective(X.data, X.indices, X.indptr, X.shape[1],
X.shape[0], M, W)