def __init__(self, n_dims, n_ls, n_kt, 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 entries of # kernel matrix for the tasks self.n_kt = n_kt # Prior for the Matern52 lengthscales self.tophat = TophatPrior(-10, 2, rng=self.rng) # Prior for the covariance amplitude self.ln_prior = LognormalPrior(mean=0.0, sigma=1.0, rng=self.rng) # Prior for the noise self.horseshoe = HorseshoePrior(scale=0.1, rng=self.rng) self.tophat_task = TophatPrior(-10, 2, rng=self.rng)
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 = LognormalPrior(sigma=0.1) # Check sampling p0 = prior.sample_from_prior(10) assert len(p0.shape) == 2 assert p0.shape[0] == 10 assert p0.shape[1] == 1
def __init__(self, n_dims): # The number of hyperparameters self.n_dims = n_dims # Prior for the Matern52 lengthscales self.tophat = TophatPrior(-2, 2) # Prior for the covariance amplitude self.ln_prior = LognormalPrior(mean=0.0, sigma=1.0) # Prior for the noise self.horseshoe = HorseshoePrior(scale=0.1)
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 # Prior for the Matern52 lengthscales self.tophat = TophatPrior(-2, 2) # Prior for the covariance amplitude self.ln_prior = LognormalPrior(mean=0.0, sigma=1.0) # Prior for the noise self.horseshoe = HorseshoePrior(scale=0.1)
def __init__(self, rng=None): """ Abstract base class to define the interface for priors of GP hyperparameter. Parameters ---------- """ if rng is None: self.rng = np.random.RandomState(np.random.randint(0, 10000)) else: self.rng = rng # Prior for the alpha self.ln_prior_alpha = LognormalPrior(sigma=0.1, mean=-10) # Prior for the sigma^2 #self.ln_prior_beta = LognormalPrior(sigma=0.1, mean=2) self.horseshoe = HorseshoePrior(scale=0.1)
def __init__(self, n_dims, n_ls, n_kt): # The number of hyperparameters self.n_dims = n_dims # The number of lengthscales self.n_ls = n_ls # The number of entries of # kernel matrix for the tasks self.n_kt = n_kt # Prior for the Matern52 lengthscales self.tophat = TophatPrior(-2, 2) # Prior for the covariance amplitude self.ln_prior = LognormalPrior(mean=0.0, sigma=1.0) # Prior for the noise self.horseshoe = HorseshoePrior(scale=0.1) self.tophat_task = TophatPrior(-2, 2)