def test_same_parameters_after_1_iters(self):
n_iters = 1
pccas = PCCASimple(latent_dim=self.k, dims=[self.p1, self.p2],
n_iters=n_iters)
pccas.Lambda1.data = self.Lambda1
pccas.Lambda2.data = self.Lambda2
pccas.Psi1_diag.data = self.Psi1_diag
pccas.Psi2_diag.data = self.Psi2_diag
pccav = PCCAVec(latent_dim=self.k, dims=[self.p1, self.p2],
n_iters=n_iters)
pccav.Lambda1.data = self.Lambda1
pccav.Lambda2.data = self.Lambda2
pccav.Psi1_diag.data = self.Psi1_diag
pccav.Psi2_diag.data = self.Psi2_diag
pccas.forward(self.y)
pccav.forward(self.y)
atol = 0.01
self.assertTrue(torch.allclose(pccas.Lambda1, pccav.Lambda1, atol=atol))
self.assertTrue(torch.allclose(pccas.Lambda2, pccav.Lambda2, atol=atol))
self.assertTrue(torch.allclose(pccas.Psi1_diag, pccav.Psi1_diag, atol=atol))
self.assertTrue(torch.allclose(pccas.Psi2_diag, pccav.Psi2_diag, atol=atol))
def test_same_nlls(self):
pccas = PCCASimple(self.k, [self.p1, self.p2], 1)
pccav = PCCAVec(self.k, [self.p1, self.p2], 1)
n_tries = 3
i = 0
nlls1 = torch.empty(n_tries)
nlls2 = torch.empty(n_tries)
while True:
if i >= n_tries:
break
# Generate new random parameters.
Lambda1 = torch.randn(self.p1, self.k)
Lambda2 = torch.randn(self.p2, self.k)
Psi1_diag = torch.randn(self.p1)
Psi2_diag = torch.randn(self.p2)
# Set each model with these new random parameters.
pccas.Lambda1.data = Lambda1
pccas.Lambda2.data = Lambda2
pccas.Psi1_diag.data = Psi1_diag
pccas.Psi2_diag.data = Psi2_diag
pccav.Lambda1.data = Lambda1
pccav.Lambda2.data = Lambda2
pccav.Psi1_diag.data = Psi1_diag
pccav.Psi2_diag.data = Psi2_diag
Lambda = torch.cat([Lambda1, Lambda2], dim=0)
Psi_diag = torch.cat([Psi1_diag, Psi2_diag])
# Compute negative log likelihood for these data and parameters.
nll1 = pccas.neg_log_likelihood(self.y, Lambda, Psi_diag)
nll2 = pccav.neg_log_likelihood(self.y, Lambda, Psi_diag)
if np.isnan(nll1) or np.isnan(nll2):
if i > 0:
i -= 1
else:
nlls1[i] = nll1
nlls2[i] = nll2
i += 1
# These are really big numbers. Close by 10 is actually good, I think.
self.assertTrue(torch.allclose(nlls1, nlls2, atol=10))
def setUp(self):
self.n = 1000
p1 = 5
p2 = 3
self.k = 2
self.p = p1 + p2
# Initialize parameters to verify that both implementations compute the
# same values.
self.P_diag = torch.diag(torch.randn(self.p, self.p)) * 10
self.L = torch.randn(self.p, self.k) * 10
self.y = torch.randn(self.p, self.n) * 10
# We only initialize these in order to access their methods.
self.pccas = PCCASimple(latent_dim=self.k, dims=[p1, p2], n_iters=1)
self.pccav = PCCAVec(latent_dim=self.k, dims=[p1, p2], n_iters=1)