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_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_ais_with_semirbm(self): rbm = SemiRBM(5, 20) rbm.L = np.matrix(np.random.randn(5, 5)) rbm.L = np.triu(rbm.L) + np.triu(rbm.L).T - 2 * np.diag(np.diag(rbm.L)) rbm.num_lateral_updates = 5 rbm.sampling_method = SemiRBM.GIBBS ais = Estimator(rbm) ais_logz = ais.estimate_log_partition_function(100, np.arange(0, 1, 0.001)) brf_logz = np.log(np.sum(np.exp(rbm._ulogprob_vis(utils.binary_numbers(rbm.X.shape[0]))))) lower = np.log(np.exp(ais_logz) - 4 * np.sqrt(rbm._ais_var)) upper = np.log(np.exp(ais_logz) + 4 * np.sqrt(rbm._ais_var)) self.assertTrue(upper - lower < 1.) self.assertTrue(lower < brf_logz and brf_logz < upper)
def test_ais_with_semirbm(self):
rbm = SemiRBM(5, 20)
rbm.L = np.matrix(np.random.randn(5, 5))
rbm.L = np.triu(rbm.L) + np.triu(rbm.L).T - 2 * np.diag(np.diag(rbm.L))
rbm.num_lateral_updates = 5
rbm.sampling_method = SemiRBM.GIBBS
ais = Estimator(rbm)
ais_logz = ais.estimate_log_partition_function(100,
np.arange(0, 1, 0.001))
brf_logz = np.log(
np.sum(
np.exp(rbm._ulogprob_vis(utils.binary_numbers(
rbm.X.shape[0])))))
lower = np.log(np.exp(ais_logz) - 4 * np.sqrt(rbm._ais_var))
upper = np.log(np.exp(ais_logz) + 4 * np.sqrt(rbm._ais_var))
self.assertTrue(upper - lower < 1.)
self.assertTrue(lower < brf_logz and brf_logz < upper)
