def test_log_S_i(self, Xa, Xb, model):
model.sample()
R = np.linspace(-np.pi, np.pi, 361)
log_S = np.empty(R.size * 2)
for i, r in enumerate(R):
Xr = Xa.copy_from_vertices()
Xr.rotate(np.degrees(r))
log_S[2*i] = log_similarity(
Xr.vertices, Xb.vertices, model.opts['S_sigma'])
Xr = Xa.copy_from_vertices()
Xr.flip(np.array([0, 1]))
Xr.rotate(np.degrees(r))
log_S[2*i + 1] = log_similarity(
Xr.vertices, Xb.vertices, model.opts['S_sigma'])
assert np.allclose(log_S, model.log_S_i)
def test_log_S_i(self, Xa, Xb, model):
model.sample()
R = model.R_i
F = model.F_i
log_S = np.empty_like(R)
for i, (r, f) in enumerate(zip(R, F)):
Xr = Xa.copy_from_vertices()
if f == 1:
Xr.flip(np.array([0, 1]))
Xr.rotate(np.degrees(r))
log_S[i] = log_similarity(
Xr.vertices, Xb.vertices, model.opts['S_sigma'])
assert np.allclose(log_S, model.log_S_i)
def test_similarity(self, Xa, Xb, model):
log_S = log_similarity(Xb.vertices, Xa.vertices, model.opts["S_sigma"])
assert log_S == model.model["log_S"].logp
log_S = log_similarity(model.model["Xb"].value, model.model["Xr"].value, model.opts["S_sigma"])
assert log_S == model.model["log_S"].logp
