def test_binomial_optimize():
random = RandomState(139)
nsamples = 30
nfeatures = 31
G = random.randn(nsamples, nfeatures) / sqrt(nfeatures)
u = random.randn(nfeatures)
z = 0.1 + 2 * dot(G, u) + random.randn(nsamples)
ntrials = random.randint(10, 500, size=nsamples)
y = zeros(nsamples)
for i in range(len(ntrials)):
y[i] = sum(
z[i] + random.logistic(scale=pi / sqrt(3), size=ntrials[i]) > 0)
(Q, S0) = economic_qs_linear(G)
M = ones((nsamples, 1))
lik = BinomialProdLik(ntrials, LogitLink())
lik.nsuccesses = y
ep = ExpFamEP(lik, M, Q[0], Q[1], S0)
ep.learn(progress=False)
assert_allclose(ep.lml(), -144.2381842202486, rtol=1e-3)
def test_binomial_get_normal_likelihood_trick():
random = RandomState(139)
nsamples = 30
nfeatures = 31
G = random.randn(nsamples, nfeatures) / sqrt(nfeatures)
u = random.randn(nfeatures)
z = 0.1 + 2 * dot(G, u) + random.randn(nsamples)
ntrials = random.randint(10, 500, size=nsamples)
y = zeros(nsamples)
for i in range(len(ntrials)):
y[i] = sum(
z[i] + random.logistic(scale=pi / sqrt(3), size=ntrials[i]) > 0)
(Q, S0) = economic_qs_linear(G)
M = ones((nsamples, 1))
lik = BinomialProdLik(ntrials, LogitLink())
lik.nsuccesses = y
ep = ExpFamEP(lik, M, Q[0], Q[1], S0)
ep.learn(progress=False)
nlt = ep.get_normal_likelihood_trick()
assert_allclose(nlt.fast_scan(G)[0], [
-143.48903288, -144.32031587, -144.03889888, -144.31806561,
-143.90248659, -144.303103, -144.47854112, -144.44469341, -144.285027,
-144.31240175, -143.11590263, -142.81623878, -141.67554141,
-144.4780024, -144.47780285, -144.10317082, -142.10043322,
-143.0813298, -143.99841663, -143.345783, -144.45458683, -144.37877612,
-142.56846859, -144.32923028, -144.44116855, -144.45082936,
-144.40932741, -143.0212886, -144.47902176, -143.94188634,
-143.72765373
],
rtol=1e-5)
