def __init__(self,
X,
likelihood,
kernel,
Z,
X_variance=None,
normalize_X=False):
GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
self.Z = Z
self.num_inducing = Z.shape[0]
self.likelihood = likelihood
if X_variance is None:
self.has_uncertain_inputs = False
else:
assert X_variance.shape == X.shape
self.has_uncertain_inputs = True
self.X_variance = X_variance
if normalize_X:
self.Z = (self.Z.copy() - self._Xoffset) / self._Xscale
# normalize X uncertainty also
if self.has_uncertain_inputs:
self.X_variance /= np.square(self._Xscale)
def __init__(self, X, likelihood, kernel, Z, q_u=None, batchsize=10, X_variance=None):
GPBase.__init__(self, X, likelihood, kernel, normalize_X=False)
self.batchsize=batchsize
self.Y = self.likelihood.Y.copy()
self.Z = Z
self.num_inducing = Z.shape[0]
self.batchcounter = 0
self.epochs = 0
self.iterations = 0
self.vb_steplength = 0.05
self.param_steplength = 1e-5
self.momentum = 0.9
if X_variance is None:
self.has_uncertain_inputs = False
else:
self.has_uncertain_inputs = True
self.X_variance = X_variance
if q_u is None:
q_u = np.hstack((np.random.randn(self.num_inducing*self.output_dim),-.5*np.eye(self.num_inducing).flatten()))
self.set_vb_param(q_u)
self._permutation = np.random.permutation(self.num_data)
self.load_batch()
self._param_trace = []
self._ll_trace = []
self._grad_trace = []
#set the adaptive steplength parameters
self.hbar_t = 0.0
self.tau_t = 100.0
self.gbar_t = 0.0
self.gbar_t1 = 0.0
self.gbar_t2 = 0.0
self.hbar_tp = 0.0
self.tau_tp = 10000.0
self.gbar_tp = 0.0
self.adapt_param_steplength = True
self.adapt_vb_steplength = True
self._param_steplength_trace = []
self._vb_steplength_trace = []
self.ensure_default_constraints()
def __init__(self, X, likelihood, kernel, Z, X_variance=None, normalize_X=False):
GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
self.Z = Z
self.num_inducing = Z.shape[0]
self.likelihood = likelihood
if X_variance is None:
self.has_uncertain_inputs = False
else:
assert X_variance.shape == X.shape
self.has_uncertain_inputs = True
self.X_variance = X_variance
if normalize_X:
self.Z = (self.Z.copy() - self._Xoffset) / self._Xscale
# normalize X uncertainty also
if self.has_uncertain_inputs:
self.X_variance /= np.square(self._Xscale)
def __init__(self, X, likelihood, kernel, normalize_X=False):
GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
self.update_likelihood_approximation()
def __init__(self, X, likelihood, kernel, normalize_X=False):
GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
self.update_likelihood_approximation()
def __init__(self, X, likelihood, kernel, normalize_X=False):
GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
self._set_params(self._get_params())
def __init__(self, X, likelihood, kernel, normalize_X=False):
GPBase.__init__(self, X, likelihood, kernel, normalize_X=normalize_X)
self._set_params(self._get_params())
