def test_sample_n_core(self, n_jobs):
n_chains = 300
n_steps = 100
tune_interval = 25
n = 4
mu1 = np.ones(n) * (1. / 2)
mu2 = -mu1
stdev = 0.1
sigma = np.power(stdev, 2) * np.eye(n)
isigma = np.linalg.inv(sigma)
dsigma = np.linalg.det(sigma)
w1 = stdev
w2 = (1 - stdev)
def last_sample(x):
return x[(n_steps - 1)::n_steps]
def two_gaussians(x):
log_like1 = - 0.5 * n * tt.log(2 * np.pi) \
- 0.5 * tt.log(dsigma) \
- 0.5 * (x - mu1).T.dot(isigma).dot(x - mu1)
log_like2 = - 0.5 * n * tt.log(2 * np.pi) \
- 0.5 * tt.log(dsigma) \
- 0.5 * (x - mu2).T.dot(isigma).dot(x - mu2)
return tt.log(w1 * tt.exp(log_like1) + w2 * tt.exp(log_like2))
with pm.Model() as ATMIP_test:
X = pm.Uniform('X',
shape=n,
lower=-2. * np.ones_like(mu1),
upper=2. * np.ones_like(mu1),
testval=-1. * np.ones_like(mu1),
transform=None)
like = pm.Deterministic('like', two_gaussians(X))
llk = pm.Potential('like_potential', like)
with ATMIP_test:
step = smc.SMC(n_chains=n_chains,
tune_interval=tune_interval,
likelihood_name=ATMIP_test.deterministics[0].name)
mtrace = smc.ATMIP_sample(n_steps=n_steps,
step=step,
n_jobs=n_jobs,
progressbar=True,
stage='0',
homepath=self.test_folder,
model=ATMIP_test,
rm_flag=True)
d = mtrace.get_values('X', combine=True, squeeze=True)
x = last_sample(d)
mu1d = np.abs(x).mean(axis=0)
np.testing.assert_allclose(mu1, mu1d, rtol=0., atol=0.03)
def setup_class(self):
super(TestSMC, self).setup_class()
self.test_folder = mkdtemp(prefix='ATMIP_TEST')
self.n_chains = 300
self.n_steps = 100
self.tune_interval = 25
n = 4
mu1 = np.ones(n) * (1. / 2)
mu2 = -mu1
stdev = 0.1
sigma = np.power(stdev, 2) * np.eye(n)
isigma = np.linalg.inv(sigma)
dsigma = np.linalg.det(sigma)
w1 = stdev
w2 = (1 - stdev)
def two_gaussians(x):
log_like1 = - 0.5 * n * tt.log(2 * np.pi) \
- 0.5 * tt.log(dsigma) \
- 0.5 * (x - mu1).T.dot(isigma).dot(x - mu1)
log_like2 = - 0.5 * n * tt.log(2 * np.pi) \
- 0.5 * tt.log(dsigma) \
- 0.5 * (x - mu2).T.dot(isigma).dot(x - mu2)
return tt.log(w1 * tt.exp(log_like1) + w2 * tt.exp(log_like2))
with pm.Model() as self.ATMIP_test:
X = pm.Uniform('X',
shape=n,
lower=-2. * np.ones_like(mu1),
upper=2. * np.ones_like(mu1),
testval=-1. * np.ones_like(mu1),
transform=None)
llk = pm.Potential('muh', two_gaussians(X))
self.step = smc.SMC(
n_chains=self.n_chains,
tune_interval=self.tune_interval,
model=self.ATMIP_test)
self.muref = mu1
def test_sample_n_core(self, n_jobs):
def last_sample(x):
return x[(self.n_steps - 1)::self.n_steps]
step = smc.SMC(n_chains=self.n_chains,
tune_interval=self.tune_interval,
model=self.ATMIP_test,
likelihood_name=self.ATMIP_test.deterministics[0].name)
mtrace = smc.ATMIP_sample(n_steps=self.n_steps,
step=step,
n_jobs=n_jobs,
progressbar=True,
homepath=self.test_folder,
model=self.ATMIP_test,
rm_flag=True)
d = mtrace.get_values('X', combine=True, squeeze=True)
x = last_sample(d)
mu1d = np.abs(x).mean(axis=0)
np.testing.assert_allclose(self.muref, mu1d, rtol=0., atol=0.03)