def test_advi_minibatch():
n = 1000
sd0 = 2.
mu0 = 4.
sd = 3.
mu = -5.
data = sd * np.random.RandomState(0).randn(n) + mu
d = n / sd**2 + 1 / sd0**2
mu_post = (n * np.mean(data) / sd**2 + mu0 / sd0**2) / d
data_t = tt.vector()
data_t.tag.test_value = np.zeros(1, )
with Model() as model:
mu_ = Normal('mu', mu=mu0, sd=sd0, testval=0)
x = Normal('x', mu=mu_, sd=sd, observed=data_t)
minibatch_RVs = [x]
minibatch_tensors = [data_t]
def create_minibatch(data):
while True:
data = np.roll(data, 100, axis=0)
yield data[:100]
minibatches = [create_minibatch(data)]
with model:
advi_fit = advi_minibatch(n=1000,
minibatch_tensors=minibatch_tensors,
minibatch_RVs=minibatch_RVs,
minibatches=minibatches,
total_size=n,
learning_rate=1e-1,
random_seed=1)
np.testing.assert_allclose(advi_fit.means['mu'], mu_post, rtol=0.1)
trace = sample_vp(advi_fit, 10000)
np.testing.assert_allclose(np.mean(trace['mu']), mu_post, rtol=0.4)
np.testing.assert_allclose(np.std(trace['mu']), np.sqrt(1. / d), rtol=0.4)
def test_advi_minibatch():
n = 1000
sd0 = 2.
mu0 = 4.
sd = 3.
mu = -5.
data = sd * np.random.RandomState(0).randn(n) + mu
d = n / sd**2 + 1 / sd0**2
mu_post = (n * np.mean(data) / sd**2 + mu0 / sd0**2) / d
data_t = tt.vector()
data_t.tag.test_value = np.zeros(1, )
with Model() as model:
mu_ = Normal('mu', mu=mu0, sd=sd0, testval=0)
x = Normal('x', mu=mu_, sd=sd, observed=data_t)
# mu = Normal('mu', mu=0, sd=1, testval=0)
# sd = HalfNormal('sd', sd=1)
# n = Normal('n', mu=mu, sd=sd, observed=data_t)
minibatch_RVs = [x]
minibatch_tensors = [data_t]
def create_minibatch(data):
while True:
data = np.roll(data, 100, axis=0)
yield data[:100]
minibatches = [create_minibatch(data)]
means, sds, elbos = advi_minibatch(model=model,
n=1000,
minibatch_tensors=minibatch_tensors,
minibatch_RVs=minibatch_RVs,
minibatches=minibatches,
total_size=n,
learning_rate=1e-1,
seed=1)
np.testing.assert_allclose(means['mu'], mu_post, rtol=0.1)
def test_advi_minibatch():
n = 1000
sd0 = 2.
mu0 = 4.
sd = 3.
mu = -5.
data = sd * np.random.RandomState(0).randn(n) + mu
d = n / sd**2 + 1 / sd0**2
mu_post = (n * np.mean(data) / sd**2 + mu0 / sd0**2) / d
data_t = tt.vector()
data_t.tag.test_value=np.zeros(1,)
with Model() as model:
mu_ = Normal('mu', mu=mu0, sd=sd0, testval=0)
x = Normal('x', mu=mu_, sd=sd, observed=data_t)
minibatch_RVs = [x]
minibatch_tensors = [data_t]
def create_minibatch(data):
while True:
data = np.roll(data, 100, axis=0)
yield data[:100]
minibatches = [create_minibatch(data)]
with model:
advi_fit = advi_minibatch(
n=1000, minibatch_tensors=minibatch_tensors,
minibatch_RVs=minibatch_RVs, minibatches=minibatches,
total_size=n, learning_rate=1e-1, random_seed=1
)
np.testing.assert_allclose(advi_fit.means['mu'], mu_post, rtol=0.1)
trace = sample_vp(advi_fit, 10000)
np.testing.assert_allclose(np.mean(trace['mu']), mu_post, rtol=0.4)
np.testing.assert_allclose(np.std(trace['mu']), np.sqrt(1. / d), rtol=0.4)
def test_advi_minibatch():
n = 1000
sd0 = 2.
mu0 = 4.
sd = 3.
mu = -5.
data = sd * np.random.RandomState(0).randn(n) + mu
d = n / sd**2 + 1 / sd0**2
mu_post = (n * np.mean(data) / sd**2 + mu0 / sd0**2) / d
data_t = tt.vector()
data_t.tag.test_value=np.zeros(1,)
with Model() as model:
mu_ = Normal('mu', mu=mu0, sd=sd0, testval=0)
x = Normal('x', mu=mu_, sd=sd, observed=data_t)
# mu = Normal('mu', mu=0, sd=1, testval=0)
# sd = HalfNormal('sd', sd=1)
# n = Normal('n', mu=mu, sd=sd, observed=data_t)
minibatch_RVs = [x]
minibatch_tensors = [data_t]
def create_minibatch(data):
while True:
data = np.roll(data, 100, axis=0)
yield data[:100]
minibatches = [create_minibatch(data)]
means, sds, elbos = advi_minibatch(
model=model, n=1000, minibatch_tensors=minibatch_tensors,
minibatch_RVs=minibatch_RVs, minibatches=minibatches,
total_size=n, learning_rate=1e-1, seed=1
)
np.testing.assert_allclose(means['mu'], mu_post, rtol=0.1)