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fixed bug in sparse GP plotting

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Nicolo Fusi 2013-05-21 16:36:12 +01:00
parent b59253fe01
commit 17a82f99bd
3 changed files with 2 additions and 33 deletions
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31
GPy/inference/SGD.py
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@ -257,37 +257,6 @@ class opt_SGD(Optimizer):
self.learning_rate = np.ones_like(self.learning_rate)*(np.dot(self.gbar_t.T, self.gbar_t) / self.hbar_t)
tau_t = self.tau_t*(1-self.learning_rate) + 1
# if t == 0:
# N = self.model.N
# Q = self.model.Q
# M = self.model.M
# iip_pos = np.arange(2*N*Q,2*N*Q+M*Q)
# mu_pos = np.arange(0,N*Q)
# S_pos = np.arange(N*Q,2*N*Q)
# self.vbparam_dict = {'iip': [iip_pos],
# 'mu': [mu_pos],
# 'S': [S_pos]}
# for k in self.vbparam_dict.keys():
# hbar_t = 0.0
# tau_t = 1.0
# gbar_t = 0.0
# self.vbparam_dict[k].append(hbar_t)
# self.vbparam_dict[k].append(tau_t)
# self.vbparam_dict[k].append(gbar_t)
# if True:
# g_t = self.model.grads
# for k in self.vbparam_dict.keys():
# pos, hbar_t, tau_t, gbar_t = self.vbparam_dict[k]
# gbar_t = (1-1/tau_t)*gbar_t + 1/tau_t * g_t[pos]
# hbar_t = (1-1/tau_t)*hbar_t + 1/tau_t * np.dot(g_t[pos].T, g_t[pos])
# self.learning_rate[pos] = (np.dot(gbar_t.T, gbar_t) / hbar_t)*1.0
# tau_t = tau_t*(1-self.learning_rate[pos]) + 1
# self.vbparam_dict[k] = [pos, hbar_t, tau_t, gbar_t]
# print k, self.learning_rate[pos].max()
def opt(self, f_fp=None, f=None, fp=None):
self.x_opt = self.model._get_params_transformed()