def test_compare_gradientless_and_gradient_learning():
seq = [1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1]
seqh = [1, 0, 1, 0, 0, 1, 0, 1, 1, 0, 1, 1, 0, 1, 1]
factors = []
hidden_factors = []
evidence = {}
o_parameters = np.array([['a', 'b'], ['c', 'd']])
h_parameters = np.array([['e', 'f'], ['g', 'h']])
for i, (s, sh) in enumerate(zip(seq, seqh)):
obs = DiscreteFactor(['o_{}'.format(i), 'h_{}'.format(i)], parameters=o_parameters)
evidence['o_{}'.format(i)] = s
evidence['h_{}'.format(i)] = sh
factors.append(obs)
if i < len(seq):
hid = DiscreteFactor(['h_{}'.format(i), 'h_{}'.format(i + 1)], parameters=h_parameters)
factors.append(hid)
hidden_factors.append(hid)
model = Model(factors)
update_order = DistributeCollectProtocol(model)
learn = LearnMrfParameters(model, update_order=update_order)
learn.fit(evidence)
print learn._optimizer_result
#print learn.iterations
ans1 = learn._optimizer_result[:2]
print
update_order = DistributeCollectProtocol(model)
learn = LearnMrfParameters(model, update_order=update_order)
fit_without_gradient(learn, evidence)
print learn._optimizer_result
#print learn.iterations
ans2 = learn._optimizer_result[:2]
assert_array_almost_equal(ans1[1], ans2[1], decimal=4)
assert_array_almost_equal(ans1[0], ans2[0], decimal=4)
def test_learn_with_gradient_binary(self):
tc1 = 70
tc2 = 14
obs = [DiscreteFactor([(i, 2)], parameters=np.array(['m0', 'm1'])) for i in xrange(tc1 + tc2)]
model = Model(obs)
evidence = dict((i, 0 if i < tc1 else 1) for i in xrange(tc1 + tc2))
print 'evidence', evidence
print model.edges
learner = LearnMrfParameters(model, prior=1.0)
def nlog_posterior(x):
c1 = tc1
c2 = tc2
x = x.reshape((2, 1))
N = np.zeros((2, 1))
Lamb = np.array([[1.0, 0], [0, 1.0]])
lp = -0.5 * np.dot(np.dot((x - N).T, Lamb), (x - N))
lp += -0.5 * x.shape[0] * np.log(2.0 * np.pi)
lp += +0.5 * np.log(np.linalg.det((Lamb)))
lp += np.dot(x.T, np.array([c1, c2])) - (c1 + c2) * np.log(sum(np.exp(x)))
return -lp[0, 0]
x0 = np.zeros(2)
print 'zeros', x0
expected_ans = scipy.optimize.fmin_l_bfgs_b(nlog_posterior, x0, approx_grad=True, pgtol=10.0**-10)
actual_ans = learner.fit(evidence).result()
print actual_ans
print expected_ans
self.assertAlmostEqual(actual_ans[0], expected_ans[1])
assert_array_almost_equal(actual_ans[1], expected_ans[0], decimal=5)
