def test_ais_with_semirbm_sanity_check(self):
grbm = GaussianRBM(15, 50)
grbm.b = np.random.randn(grbm.b.shape[0], 1)
grbm.c = np.random.randn(grbm.c.shape[0], 1)
srbm = SemiRBM(50, 20)
srbm.W = srbm.W * 0.
srbm.c = srbm.c * 0.
srbm.L = grbm.W.T * grbm.W
srbm.b = grbm.W.T * grbm.b + grbm.c + 0.5 * np.matrix(np.diag(
srbm.L)).T
srbm.L = srbm.L - np.matrix(np.diag(np.diag(srbm.L)))
ais = Estimator(grbm)
ais.estimate_log_partition_function(num_ais_samples=100,
beta_weights=np.arange(0, 1, 1E-3))
ais = Estimator(srbm)
ais.estimate_log_partition_function(num_ais_samples=100,
beta_weights=np.arange(0, 1, 1E-2))
glogz = grbm._ais_logz + srbm.Y.shape[0] * np.log(2)
slogz = srbm._ais_logz + grbm.X.shape[0] * np.log(np.sqrt(2 * np.pi))
self.assertTrue(np.abs(glogz - slogz) < 1.)
def test_all_pairs(self): srbm = SemiRBM(10, 10) srbm.W = np.matrix(np.random.randn(srbm.X.shape[0], srbm.Y.shape[0])) srbm.b = np.matrix(np.random.rand(srbm.X.shape[0], 1)) srbm.c = np.matrix(np.random.randn(srbm.Y.shape[0], 1)) examples_vis = np.matrix(np.random.rand(srbm.X.shape[0], 100) < 0.5) examples_hid = np.matrix(np.random.rand(srbm.Y.shape[0], 100) < 0.5) logprob1 = srbm._ulogprob(examples_vis, examples_hid) logprob2 = np.diag(srbm._ulogprob(examples_vis, examples_hid, all_pairs=True)) self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = srbm._ulogprob(examples_vis[:, 1], examples_hid) logprob2 = srbm._ulogprob(examples_vis, examples_hid, all_pairs=True)[1, :] self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = srbm._ulogprob(examples_vis, examples_hid[:, 1]) logprob2 = srbm._ulogprob(examples_vis, examples_hid, all_pairs=True)[:, 1].T self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = srbm._clogprob_hid_vis(examples_vis, examples_hid) logprob2 = np.diag(srbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True)) self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = srbm._clogprob_hid_vis(examples_vis[:, 1], examples_hid) logprob2 = srbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True)[1, :] self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10) logprob1 = srbm._clogprob_hid_vis(examples_vis, examples_hid[:, 1]) logprob2 = srbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True)[:, 1].T self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10)
def test_probabilities(self):
srbm = SemiRBM(9, 12)
srbm.W = np.matrix(np.random.randn(srbm.X.shape[0], srbm.Y.shape[0]))
srbm.b = np.matrix(np.random.rand(srbm.X.shape[0], 1))
srbm.c = np.matrix(np.random.randn(srbm.Y.shape[0], 1))
srbm.L = np.matrix(np.random.randn(srbm.X.shape[0],
srbm.X.shape[0])) / 2.
srbm.L = np.triu(srbm.L) + np.triu(
srbm.L).T - 2. * np.diag(np.diag(srbm.L))
examples_vis = np.matrix(np.random.rand(srbm.X.shape[0], 100) < 0.5)
examples_hid = np.matrix(np.random.rand(srbm.Y.shape[0], 100) < 0.5)
states_vis = utils.binary_numbers(srbm.X.shape[0])
states_hid = utils.binary_numbers(srbm.Y.shape[0])
# check that conditional probabilities are normalized
logprobs = srbm._clogprob_hid_vis(examples_vis,
states_hid,
all_pairs=True)
self.assertTrue(np.all(utils.logsumexp(logprobs, 1) < 1E-10))
# test for consistency
logprobs1 = srbm._ulogprob(examples_vis, examples_hid, all_pairs=True)
logprobs3 = srbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True) \
+ srbm._ulogprob_vis(examples_vis).T
self.assertTrue(np.all(np.abs(logprobs1 - logprobs3) < 1E-10))
rbm = RBM(srbm.X.shape[0], srbm.Y.shape[0])
rbm.W = srbm.W
rbm.b = srbm.b
rbm.c = srbm.c
srbm.L *= 0
logprobs1 = rbm._ulogprob_vis(examples_vis)
logprobs2 = srbm._ulogprob_vis(examples_vis)
self.assertTrue(np.all(np.abs(logprobs1 - logprobs2) < 1E-10))
logprobs1 = rbm._clogprob_hid_vis(examples_vis, examples_hid)
logprobs2 = srbm._clogprob_hid_vis(examples_vis, examples_hid)
self.assertTrue(np.all(np.abs(logprobs1 - logprobs2) < 1E-10))
def test_all_pairs(self):
srbm = SemiRBM(10, 10)
srbm.W = np.matrix(np.random.randn(srbm.X.shape[0], srbm.Y.shape[0]))
srbm.b = np.matrix(np.random.rand(srbm.X.shape[0], 1))
srbm.c = np.matrix(np.random.randn(srbm.Y.shape[0], 1))
examples_vis = np.matrix(np.random.rand(srbm.X.shape[0], 100) < 0.5)
examples_hid = np.matrix(np.random.rand(srbm.Y.shape[0], 100) < 0.5)
logprob1 = srbm._ulogprob(examples_vis, examples_hid)
logprob2 = np.diag(
srbm._ulogprob(examples_vis, examples_hid, all_pairs=True))
self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10)
logprob1 = srbm._ulogprob(examples_vis[:, 1], examples_hid)
logprob2 = srbm._ulogprob(examples_vis, examples_hid,
all_pairs=True)[1, :]
self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10)
logprob1 = srbm._ulogprob(examples_vis, examples_hid[:, 1])
logprob2 = srbm._ulogprob(examples_vis, examples_hid,
all_pairs=True)[:, 1].T
self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10)
logprob1 = srbm._clogprob_hid_vis(examples_vis, examples_hid)
logprob2 = np.diag(
srbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True))
self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10)
logprob1 = srbm._clogprob_hid_vis(examples_vis[:, 1], examples_hid)
logprob2 = srbm._clogprob_hid_vis(examples_vis,
examples_hid,
all_pairs=True)[1, :]
self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10)
logprob1 = srbm._clogprob_hid_vis(examples_vis, examples_hid[:, 1])
logprob2 = srbm._clogprob_hid_vis(examples_vis,
examples_hid,
all_pairs=True)[:, 1].T
self.assertTrue(np.abs(logprob1 - logprob2).sum() < 1E-10)
def test_ais_with_semirbm_sanity_check(self): grbm = GaussianRBM(15, 50) grbm.b = np.random.randn(grbm.b.shape[0], 1) grbm.c = np.random.randn(grbm.c.shape[0], 1) srbm = SemiRBM(50, 20) srbm.W = srbm.W * 0. srbm.c = srbm.c * 0. srbm.L = grbm.W.T * grbm.W srbm.b = grbm.W.T * grbm.b + grbm.c + 0.5 * np.matrix(np.diag(srbm.L)).T srbm.L = srbm.L - np.matrix(np.diag(np.diag(srbm.L))) ais = Estimator(grbm) ais.estimate_log_partition_function(num_ais_samples=100, beta_weights=np.arange(0, 1, 1E-3)) ais = Estimator(srbm) ais.estimate_log_partition_function(num_ais_samples=100, beta_weights=np.arange(0, 1, 1E-2)) glogz = grbm._ais_logz + srbm.Y.shape[0] * np.log(2) slogz = srbm._ais_logz + grbm.X.shape[0] * np.log(np.sqrt(2 * np.pi)) self.assertTrue(np.abs(glogz - slogz) < 1.)
def test_probabilities(self): srbm = SemiRBM(9, 12) srbm.W = np.matrix(np.random.randn(srbm.X.shape[0], srbm.Y.shape[0])) srbm.b = np.matrix(np.random.rand(srbm.X.shape[0], 1)) srbm.c = np.matrix(np.random.randn(srbm.Y.shape[0], 1)) srbm.L = np.matrix(np.random.randn(srbm.X.shape[0], srbm.X.shape[0])) / 2. srbm.L = np.triu(srbm.L) + np.triu(srbm.L).T - 2. * np.diag(np.diag(srbm.L)) examples_vis = np.matrix(np.random.rand(srbm.X.shape[0], 100) < 0.5) examples_hid = np.matrix(np.random.rand(srbm.Y.shape[0], 100) < 0.5) states_vis = utils.binary_numbers(srbm.X.shape[0]) states_hid = utils.binary_numbers(srbm.Y.shape[0]) # check that conditional probabilities are normalized logprobs = srbm._clogprob_hid_vis(examples_vis, states_hid, all_pairs=True) self.assertTrue(np.all(utils.logsumexp(logprobs, 1) < 1E-10)) # test for consistency logprobs1 = srbm._ulogprob(examples_vis, examples_hid, all_pairs=True) logprobs3 = srbm._clogprob_hid_vis(examples_vis, examples_hid, all_pairs=True) \ + srbm._ulogprob_vis(examples_vis).T self.assertTrue(np.all(np.abs(logprobs1 - logprobs3) < 1E-10)) rbm = RBM(srbm.X.shape[0], srbm.Y.shape[0]) rbm.W = srbm.W rbm.b = srbm.b rbm.c = srbm.c srbm.L *= 0 logprobs1 = rbm._ulogprob_vis(examples_vis) logprobs2 = srbm._ulogprob_vis(examples_vis) self.assertTrue(np.all(np.abs(logprobs1 - logprobs2) < 1E-10)) logprobs1 = rbm._clogprob_hid_vis(examples_vis, examples_hid) logprobs2 = srbm._clogprob_hid_vis(examples_vis, examples_hid) self.assertTrue(np.all(np.abs(logprobs1 - logprobs2) < 1E-10))
