def _build(self):
self.params = {}
self.latent_values = None
# Build the transformer
beta_warp = BetaWarp(self.num_dims)
beta_alpha, beta_beta = beta_warp.hypers
self.params['beta_alpha'] = beta_alpha
self.params['beta_beta'] = beta_beta
transformer = Transformer(self.num_dims)
transformer.add_layer(beta_warp)
# Build the component kernels
input_kernel = Matern52(self.num_dims)
ls = input_kernel.hypers
self.params['ls'] = ls
# Now apply the transformation.
transform_kernel = TransformKernel(input_kernel, transformer)
# Add some perturbation for stability
stability_noise = Noise(self.num_dims)
# Finally make a noisy version if necessary
# In a classifier GP the notion of "noise" is really just the scale.
if self.noiseless:
self._kernel = SumKernel(transform_kernel, stability_noise)
else:
scaled_kernel = Scale(transform_kernel)
self._kernel = SumKernel(scaled_kernel, stability_noise)
amp2 = scaled_kernel.hypers
self.params['amp2'] = amp2
# Build the mean function (just a constant mean for now)
self.mean = Hyperparameter(
initial_value = 0.0,
prior = priors.Gaussian(0.0,1.0),
name = 'mean'
)
self.params['mean'] = self.mean
# Buld the latent values. Empty for now until the GP gets data.
self.latent_values = Hyperparameter(
initial_value = np.array([]),
name = 'latent values'
)
# Build the samplers
to_sample = [self.mean] if self.noiseless else [self.mean, amp2]
self._samplers.append(SliceSampler(*to_sample, compwise=False, thinning=self.thinning))
self._samplers.append(WhitenedPriorSliceSampler(ls, beta_alpha, beta_beta, compwise=True, thinning=self.thinning))
self.latent_values_sampler = EllipticalSliceSampler(self.latent_values, thinning=self.ess_thinning)
def _build(self):
# Build the transformer
beta_warp = BetaWarp(self.num_dims)
transformer = Transformer(self.num_dims)
transformer.add_layer(beta_warp)
# Build the component kernels
input_kernel = Matern52(self.num_dims)
stability_noise_kernel = Noise(self.num_dims) # Even if noiseless we use some noise for stability
scaled_input_kernel = Scale(input_kernel)
sum_kernel = SumKernel(scaled_input_kernel, stability_noise_kernel)
noise_kernel = Noise(self.num_dims)
# The final kernel applies the transformation.
self._kernel = TransformKernel(sum_kernel, transformer)
# Finally make a noisy version if necessary
if not self.noiseless:
self._kernel_with_noise = SumKernel(self._kernel, noise_kernel)
# Build the mean function (just a constant mean for now)
self.mean = Hyperparameter(
initial_value = 0.0,
prior = priors.Gaussian(0.0,1.0),
name = 'mean'
)
# Get the hyperparameters to sample
ls = input_kernel.hypers
amp2 = scaled_input_kernel.hypers
beta_alpha, beta_beta = beta_warp.hypers
self.params = {
'mean' : self.mean,
'amp2' : amp2,
'ls' : ls,
'beta_alpha' : beta_alpha,
'beta_beta' : beta_beta
}
# Build the samplers
if self.noiseless:
self._samplers.append(SliceSampler(self.mean, amp2, compwise=False, thinning=self.thinning))
else:
noise = noise_kernel.hypers
self.params.update({'noise' : noise})
self._samplers.append(SliceSampler(self.mean, amp2, noise, compwise=False, thinning=self.thinning))
self._samplers.append(SliceSampler(ls, beta_alpha, beta_beta, compwise=True, thinning=self.thinning))
def __init__(self, num_dims, weights=None, num_factors=2, name="Linear"):
self.name = name
self.num_dims = num_dims
self.num_factors = num_factors if not weights else int(weights.shape[0] / num_dims)
if weights:
assert self.num_factors*self.num_dims == weights.shape[0]
else:
default_weights = Hyperparameter(
initial_value = 0.1*np.random.randn(num_dims*num_factors),
prior = priors.Gaussian(0,1),
name = 'weights'
)
self.weights = weights if weights is not None else default_weights
if __name__ == '__main__':
sys.path.append('..')
from utils import priors
import matplotlib.pyplot as plt
n = 10000
# Test on 1D Gaussian
x_samples = np.zeros(n)
x = np.zeros(1)
gsn = priors.Gaussian(mu=-1, sigma=4)
for i in xrange(n):
if i % 1000 == 0:
print('Sample %d/%d' % (i, n))
x, cur_ll = slice_sample(x, gsn.logprob)
x_samples[i] = x.copy()
print('1D Gaussian actual mean: %f, mean of samples: %f' %
(-1, np.mean(x_samples)))
print('1D Gaussian actual sigma: %f, std of samples: %f' %
(4, np.std(x_samples)))
plt.figure(1)
plt.clf()