def __init__(self, n_dims, n_ls, n_lr, rng=None):
if rng is None:
self.rng = np.random.RandomState(np.random.randint(0, 10000))
else:
self.rng = rng
# The number of hyperparameters
self.n_dims = n_dims
# The number of lengthscales
self.n_ls = n_ls
# The number of params of the bayes linear reg kernel
self.n_lr = n_lr
self.bayes_lin_prior = NormalPrior(sigma=1, mean=0, rng=self.rng)
# Prior for the Matern52 lengthscales
self.tophat = TophatPrior(-10, 2, rng=self.rng)
# Prior for the covariance amplitude
self.ln_prior = LognormalPrior(mean=-2, sigma=1.0, rng=self.rng)
# Prior for the noise
self.horseshoe = HorseshoePrior(scale=0.001, rng=self.rng)
def test(self):
prior = NormalPrior(mean=0, sigma=1)
# Check sampling
p0 = prior.sample_from_prior(10)
assert len(p0.shape) == 2
assert p0.shape[0] == 10
assert p0.shape[1] == 1
# Check gradients
x_ = np.array([[0.1]])
assert check_grad(prior.lnprob, prior.gradient, x_) <= 1e-5
def test(self):
prior = NormalPrior(mean=0, sigma=1)
# Check sampling
p0 = prior.sample_from_prior(10)
assert len(p0.shape) == 2
assert p0.shape[0] == 10
assert p0.shape[1] == 1
# Check gradients
x_ = np.array([[0.1]])
assert check_grad(prior.lnprob, prior.gradient, x_) <= 1e-5
def __init__(self, n_dims, n_ls, n_lr, rng=None):
if rng is None:
self.rng = np.random.RandomState(np.random.randint(0, 10000))
else:
self.rng = rng
# The number of hyperparameters
self.n_dims = n_dims
# The number of lengthscales
self.n_ls = n_ls
# The number of params of the bayes linear reg kernel
self.n_lr = n_lr
self.bayes_lin_prior = NormalPrior(sigma=1, mean=0, rng=self.rng)
# Prior for the Matern52 lengthscales
self.tophat = TophatPrior(-10, 2, rng=self.rng)
# Prior for the covariance amplitude
self.ln_prior = LognormalPrior(mean=-2, sigma=1.0, rng=self.rng)
# Prior for the noise
self.horseshoe = HorseshoePrior(scale=0.001, rng=self.rng)
def __init__(self, n_dims, n_ls, n_lr):
# The number of hyperparameters
self.n_dims = n_dims
# The number of lengthscales
self.n_ls = n_ls
# The number of params of the bayes linear reg kernel
self.n_lr = n_lr
self.bayes_lin_prior = NormalPrior(sigma=1, mean=0)
# Prior for the Matern52 lengthscales
self.tophat = TophatPrior(-2, 2)
# Prior for the covariance amplitude
self.ln_prior = LognormalPrior(mean=-2, sigma=1.0)
# Prior for the noise
self.horseshoe = HorseshoePrior(scale=0.001)
def __init__(self, n_dims, n_ls, n_lr):
# The number of hyperparameters
self.n_dims = n_dims
# The number of lengthscales
self.n_ls = n_ls
# The number of params of the bayes linear reg kernel
self.n_lr = n_lr
self.bayes_lin_prior = NormalPrior(sigma=1, mean=0)
# Prior for the Matern52 lengthscales
self.tophat = TophatPrior(-2, 2)
# Prior for the covariance amplitude
self.ln_prior = LognormalPrior(mean=-2, sigma=1.0)
# Prior for the noise
self.horseshoe = HorseshoePrior(scale=0.001)
class EnvPrior(BasePrior):
def __init__(self, n_dims, n_ls, n_lr, rng=None):
if rng is None:
self.rng = np.random.RandomState(np.random.randint(0, 10000))
else:
self.rng = rng
# The number of hyperparameters
self.n_dims = n_dims
# The number of lengthscales
self.n_ls = n_ls
# The number of params of the bayes linear reg kernel
self.n_lr = n_lr
self.bayes_lin_prior = NormalPrior(sigma=1, mean=0, rng=self.rng)
# Prior for the Matern52 lengthscales
self.tophat = TophatPrior(-10, 2, rng=self.rng)
# Prior for the covariance amplitude
self.ln_prior = LognormalPrior(mean=-2, sigma=1.0, rng=self.rng)
# Prior for the noise
self.horseshoe = HorseshoePrior(scale=0.001, rng=self.rng)
def lnprob(self, theta):
lp = 0
# Covariance amplitude
lp += self.ln_prior.lnprob(theta[0])
# Lengthscales
lp += self.tophat.lnprob(theta[1:self.n_ls + 1])
# Prior for the Bayesian regression kernel
#pos = (self.n_ls + 1)
#end = (self.n_ls + self.n_lr + 1)
#for t in theta[pos:end]:
# lp += self.bayes_lin_prior.lnprob(t)
# Noise
lp += self.horseshoe.lnprob(theta[-1])
return lp
def sample_from_prior(self, n_samples):
p0 = np.zeros([n_samples, self.n_dims])
# Covariance amplitude
p0[:, 0] = self.ln_prior.sample_from_prior(n_samples)[:, 0]
# Lengthscales
ls_sample = np.array([self.tophat.sample_from_prior(n_samples)[:, 0]
for _ in range(0, self.n_ls)]).T
p0[:, 1:(self.n_ls + 1)] = ls_sample
# Bayesian linear regression
pos = (self.n_ls + 1)
end = (self.n_ls + self.n_lr + 1)
samples = np.array([self.bayes_lin_prior.sample_from_prior(n_samples)[:, 0]
for _ in range(0, self.n_lr)]).T
p0[:, pos:end] = samples
# Noise
p0[:, -1] = self.horseshoe.sample_from_prior(n_samples)[:, 0]
return p0
class EnvPrior(BasePrior):
def __init__(self, n_dims, n_ls, n_lr):
# The number of hyperparameters
self.n_dims = n_dims
# The number of lengthscales
self.n_ls = n_ls
# The number of params of the bayes linear reg kernel
self.n_lr = n_lr
self.bayes_lin_prior = NormalPrior(sigma=1, mean=0)
# Prior for the Matern52 lengthscales
self.tophat = TophatPrior(-2, 2)
# Prior for the covariance amplitude
self.ln_prior = LognormalPrior(mean=-2, sigma=1.0)
# Prior for the noise
self.horseshoe = HorseshoePrior(scale=0.001)
def lnprob(self, theta):
lp = 0
# Covariance amplitude
lp += self.ln_prior.lnprob(theta[0])
# Lengthscales
lp += self.tophat.lnprob(theta[1:self.n_ls + 1])
# Prior for the Bayesian regression kernel
pos = (self.n_ls + 1)
end = (self.n_ls + self.n_lr + 1)
#lp += -np.sum((theta[pos:end]) ** 2 / 10.)
for t in theta[pos:end]:
lp += self.bayes_lin_prior.lnprob(t)
# Noise
lp += self.horseshoe.lnprob(theta[-1])
return lp
def sample_from_prior(self, n_samples):
p0 = np.zeros([n_samples, self.n_dims])
# Covariance amplitude
p0[:, 0] = self.ln_prior.sample_from_prior(n_samples)[:, 0]
# Lengthscales
ls_sample = np.array([
self.tophat.sample_from_prior(n_samples)[:, 0]
for _ in range(0, (self.n_ls))
]).T
p0[:, 1:(self.n_ls + 1)] = ls_sample
# Bayesian linear regression
pos = (self.n_ls + 1)
end = (self.n_ls + self.n_lr + 1)
#p0[:, pos:end] = np.array([np.random.randn(n_samples)
# for _ in range(0, (self.n_lr))]).T
samples = np.array([
self.bayes_lin_prior.sample_from_prior(n_samples)[:, 0]
for _ in range(0, (self.n_lr))
]).T
p0[:, pos:end] = samples
# Noise
p0[:, -1] = self.horseshoe.sample_from_prior(n_samples)[:, 0]
return p0
class EnvPrior(BasePrior):
def __init__(self, n_dims, n_ls, n_lr):
# The number of hyperparameters
self.n_dims = n_dims
# The number of lengthscales
self.n_ls = n_ls
# The number of params of the bayes linear reg kernel
self.n_lr = n_lr
self.bayes_lin_prior = NormalPrior(sigma=1, mean=0)
# Prior for the Matern52 lengthscales
self.tophat = TophatPrior(-2, 2)
# Prior for the covariance amplitude
self.ln_prior = LognormalPrior(mean=-2, sigma=1.0)
# Prior for the noise
self.horseshoe = HorseshoePrior(scale=0.001)
def lnprob(self, theta):
lp = 0
# Covariance amplitude
lp += self.ln_prior.lnprob(theta[0])
# Lengthscales
lp += self.tophat.lnprob(theta[1:self.n_ls + 1])
# Prior for the Bayesian regression kernel
pos = (self.n_ls + 1)
end = (self.n_ls + self.n_lr + 1)
#lp += -np.sum((theta[pos:end]) ** 2 / 10.)
for t in theta[pos:end]:
lp += self.bayes_lin_prior.lnprob(t)
# Noise
lp += self.horseshoe.lnprob(theta[-1])
return lp
def sample_from_prior(self, n_samples):
p0 = np.zeros([n_samples, self.n_dims])
# Covariance amplitude
p0[:, 0] = self.ln_prior.sample_from_prior(n_samples)[:, 0]
# Lengthscales
ls_sample = np.array([self.tophat.sample_from_prior(n_samples)[:, 0]
for _ in range(0, (self.n_ls))]).T
p0[:, 1:(self.n_ls + 1)] = ls_sample
# Bayesian linear regression
pos = (self.n_ls + 1)
end = (self.n_ls + self.n_lr + 1)
#p0[:, pos:end] = np.array([np.random.randn(n_samples)
# for _ in range(0, (self.n_lr))]).T
samples = np.array([self.bayes_lin_prior.sample_from_prior(n_samples)[:, 0]
for _ in range(0, (self.n_lr))]).T
p0[:, pos:end] = samples
# Noise
p0[:, -1] = self.horseshoe.sample_from_prior(n_samples)[:, 0]
return p0