add tests for cond and cens likelihood scores

tests/test_logs.py

@@ -33,6 +33,43 @@ def test_ensemble(backend):
     assert np.isclose(res, expected)
 
 
+@pytest.mark.parametrize("backend", BACKENDS)
+def test_clogs(backend):
+    obs = np.random.randn(N)
+    mu = obs + np.random.randn(N) * 0.1
+    sigma = abs(np.random.randn(N))
+    fct = np.random.randn(N, ENSEMBLE_SIZE) * sigma[..., None] + mu[..., None]
+
+    res0 = _logs.logs_ensemble(obs, fct, axis=-1, backend=backend)
+    res = _logs.clogs_ensemble(obs, fct, axis=-1, backend=backend)
+    res_co = _logs.clogs_ensemble(obs, fct, axis=-1, cens=False, backend=backend)
+    assert res.shape == (N,)
+    assert res_co.shape == (N,)
+    assert np.allclose(res, res0)
+    assert np.allclose(res_co, res0)
+
+    fct = fct.T
+    res0 = _logs.clogs_ensemble(obs, fct, axis=0, backend=backend)
+    assert np.allclose(res, res0)
+
+    obs, fct = 6.2, [4.2, 5.1, 6.1, 7.6, 8.3, 9.5]
+    a = 8.0
+    res = _logs.clogs_ensemble(obs, fct, a=a, backend=backend)
+    expected = 0.3929448
+    assert np.isclose(res, expected)
+
+    a = 5.0
+    res = _logs.clogs_ensemble(obs, fct, a=a, cens=False, backend=backend)
+    expected = 1.646852
+    assert np.isclose(res, expected)
+
+    fct = [-142.7, -160.3, -179.4, -184.5]
+    b = -150.0
+    res = _logs.clogs_ensemble(obs, fct, b=b, backend=backend)
+    expected = 1.420427
+    assert np.isclose(res, expected)
+
+
 @pytest.mark.parametrize("backend", BACKENDS)
 def test_beta(backend):
     if backend == "torch":