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Small changes in svigp

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Andreas 2013-12-13 14:01:01 +00:00
parent 958e9f7c7a
commit 4d82f30367
1 changed files with 13 additions and 8 deletions
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21
GPy/core/svigp.py
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@ -52,7 +52,6 @@ class SVIGP(GPBase):
self.Y = self.likelihood.Y.copy()
self.Z = Z
self.num_inducing = Z.shape[0]
self.batchcounter = 0
self.epochs = 0
self.iterations = 0
@ -318,12 +317,12 @@ class SVIGP(GPBase):
#Iterate!
for i in range(iterations):
#store the current configuration for plotting later
self._param_trace.append(self._get_params())
self._ll_trace.append(self.log_likelihood() + self.log_prior())
#load a batch
#load a batch and do the appropriate computations (kernel matrices, etc)
self.load_batch()
#compute the (stochastic) gradient
@ -333,7 +332,8 @@ class SVIGP(GPBase):
#compute the steps in all parameters
vb_step = self.vb_steplength*natgrads[0]
if (self.epochs>=1):#only move the parameters after the first epoch
#only move the parameters after the first epoch and only if the steplength is nonzero
if (self.epochs>=1) and (self.param_steplength > 0):
param_step = self.momentum*param_step + self.param_steplength*grads
else:
param_step = 0.
@ -355,6 +355,8 @@ class SVIGP(GPBase):
if self.epochs > 10:
self._adapt_steplength()
self._vb_steplength_trace.append(self.vb_steplength)
self._param_steplength_trace.append(self.param_steplength)
self.iterations += 1
@ -363,17 +365,20 @@ class SVIGP(GPBase):
if self.adapt_vb_steplength:
# self._adaptive_vb_steplength()
self._adaptive_vb_steplength_KL()
self._vb_steplength_trace.append(self.vb_steplength)
assert self.vb_steplength > 0
#self._vb_steplength_trace.append(self.vb_steplength)
assert self.vb_steplength >= 0
if self.adapt_param_steplength:
self._adaptive_param_steplength()
# self._adaptive_param_steplength_log()
# self._adaptive_param_steplength_from_vb()
self._param_steplength_trace.append(self.param_steplength)
#self._param_steplength_trace.append(self.param_steplength)
def _adaptive_param_steplength(self):
decr_factor = 0.02
if hasattr(self, 'adapt_param_steplength_decr'):
decr_factor = self.adapt_param_steplength_decr
else:
decr_factor = 0.02
g_tp = self._transform_gradients(self._log_likelihood_gradients())
self.gbar_tp = (1-1/self.tau_tp)*self.gbar_tp + 1/self.tau_tp * g_tp
self.hbar_tp = (1-1/self.tau_tp)*self.hbar_tp + 1/self.tau_tp * np.dot(g_tp.T, g_tp)