def test_p_is_close(self):
P = p_from_latent(self.X, self.Y, rescale=False, loops=True)
if any(P[P > 1]) or any(P[P < -1]): # wrong values for P
raise ValueError("P values should be less than 1 and bigger than -1")
np.random.seed(8888)
graphs1 = []
graphs2 = []
for i in range(1000):
g1, g2 = rdpg_corr(
self.X, self.Y, self.r, rescale=False, directed=True, loops=True
)
graphs1.append(g1)
graphs2.append(g2)
graphs1 = np.stack(graphs1)
graphs2 = np.stack(graphs2)
np.testing.assert_allclose(np.mean(graphs1, axis=0), P, atol=0.1)
np.testing.assert_allclose(np.mean(graphs2, axis=0), P, atol=0.1)
def test_inputs(self):
x1 = np.array([[1, 1], [1, 1]])
x2 = np.array([[1, 1]])
x3 = np.zeros((2, 2, 2))
with self.assertRaises(TypeError):
p_from_latent("hi") # wrong type
with self.assertRaises(ValueError):
p_from_latent(x1, x2) # dimension mismatch
with self.assertRaises(ValueError):
p_from_latent(x3) # wrong num dimensions
with self.assertRaises(TypeError):
sample_edges("XD") # wrong type
with self.assertRaises(ValueError):
sample_edges(x3) # wrong num dimensions
with self.assertRaises(ValueError):
sample_edges(x2) # wrong shape for P
if any(self.X[self.X > 1]) or any(self.X[self.X < -1]): # wrong values for P
raise ValueError("P values should be less than 1 and bigger than -1")