def test_normal_sampling():
for mean in [0, 1]:
dist = Normal(mean, 3 * B.eye(np.int32, 200))
# Sample without noise.
samples = dist.sample(2000)
approx(B.mean(samples), mean, atol=5e-2)
approx(B.std(samples)**2, 3, atol=5e-2)
# Sample with noise
samples = dist.sample(2000, noise=2)
approx(B.mean(samples), mean, atol=5e-2)
approx(B.std(samples)**2, 5, atol=5e-2)
def test_normal_sampling():
for mean in [0, 1]:
dist = Normal(mean, 3 * B.eye(np.int32, 200))
# Sample without noise.
samples = dist.sample(2000)
approx(B.mean(samples), mean, atol=5e-2)
approx(B.std(samples) ** 2, 3, atol=5e-2)
# Sample with noise
samples = dist.sample(2000, noise=2)
approx(B.mean(samples), mean, atol=5e-2)
approx(B.std(samples) ** 2, 5, atol=5e-2)
state, sample1 = dist.sample(B.create_random_state(B.dtype(dist), seed=0))
state, sample2 = dist.sample(B.create_random_state(B.dtype(dist), seed=0))
assert isinstance(state, B.RandomState)
approx(sample1, sample2)
def test_normalise():
layer = Normalise(epsilon=0)
x = B.randn(10, 5, 3)
# Check number of weights and width.
assert layer.num_weights(10) == 0
assert layer.width == 10
# Check initialisation and width.
layer.initialise(3, None)
assert layer.width == 3
# Check correctness
out = layer(x)
approx(B.std(out, axis=2), B.ones(10, 5), rtol=1e-4)
approx(B.mean(out, axis=2), B.zeros(10, 5), atol=1e-4)
def summarise_samples(x, samples, db=False):
"""Summarise samples.
Args:
x (vector): Inputs of samples.
samples (tensor): Samples, with the first dimension corresponding
to different samples.
db (bool, optional): Convert to decibels.
Returns:
:class:`collections.namedtuple`: Named tuple containing various
statistics of the samples.
"""
x, samples = B.to_numpy(x, samples)
random_inds = np.random.permutation(B.shape(samples)[0])[:3]
def transform(x):
if db:
return 10 * np.log10(x)
else:
return x
perm = tuple(reversed(range(B.rank(samples)))) # Reverse all dimensions.
return collect(
x=B.to_numpy(x),
mean=transform(B.mean(samples, axis=0)),
var=transform(B.std(samples, axis=0))**2,
err_68_lower=transform(B.quantile(samples, 0.32, axis=0)),
err_68_upper=transform(B.quantile(samples, 1 - 0.32, axis=0)),
err_95_lower=transform(B.quantile(samples, 0.025, axis=0)),
err_95_upper=transform(B.quantile(samples, 1 - 0.025, axis=0)),
err_99_lower=transform(B.quantile(samples, 0.0015, axis=0)),
err_99_upper=transform(B.quantile(samples, 1 - 0.0015, axis=0)),
samples=transform(B.transpose(samples, perm=perm)[..., random_inds]),
all_samples=transform(B.transpose(samples, perm=perm)),
)
def test_sample_noisy(construct_oilmm, x):
oilmm = construct_oilmm(noise_amplification=1000)
sample = oilmm.sample(x, latent=False)
# Test that sample has high variance.
assert B.std(sample) > 10
def test_sample_noiseless(construct_oilmm, x):
oilmm = construct_oilmm(noise_amplification=1000)
sample = oilmm.sample(x, latent=True)
# Test that sample has low variance.
assert B.std(sample) < 4
import lab as B
import numpy as np
from experiments.experiment import run, setup
from wbml.data.mauna_loa import load
args, wd = setup("mauna_loa")
n = 200
data = load(detrend_method="gp")
t = np.array(data.index)[-n:]
t = t - t[0]
y = np.array(data["ppm_detrended"])[-n:]
# Normalise to zero mean and unity variance.
y -= B.mean(y)
y /= B.std(y)
# Setup GPCM models.
noise = 0.05
window = 5
scale = 1 / 12
run(
args=args,
wd=wd,
noise=noise,
window=window,
scale=scale,
fix_window_scale=True,
t=t,
y=y,
def __call__(self, x):
mean = B.mean(x, axis=2)[:, :, None]
std = B.std(x, axis=2)[:, :, None]
return (x - mean) / (std + self.epsilon)