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Fariba 2015-07-27 17:30:24 +01:00
parent 9b54f831b3
commit e785668417
2 changed files with 8 additions and 6 deletions
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10
GPy/core/parameterization/priors.py
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@ -843,7 +843,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
# Calculating beta and Bi for Sb # Calculating beta and Bi for Sb
def compute_sig_beta_Bi(self, data_idx, M_i, M_0, lst_idx_all): def compute_sig_beta_Bi(self, data_idx, M_i, M_0, lst_idx_all):
import pdb # import pdb
# pdb.set_trace() # pdb.set_trace()
B_i = np.zeros((self.classnum, self.dim)) B_i = np.zeros((self.classnum, self.dim))
Sig_beta_B_i_all = np.zeros((self.datanum, self.dim)) Sig_beta_B_i_all = np.zeros((self.datanum, self.dim))
@ -909,8 +909,8 @@ class DGPLVM_Lamda(Prior, Parameterized):
Sw = self.compute_Sw(cls, M_i) Sw = self.compute_Sw(cls, M_i)
# Sb_inv_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1)) # Sb_inv_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))
#Sb_inv_N = np.linalg.inv(Sb+np.eye(Sb.shape[0])*0.1) #Sb_inv_N = np.linalg.inv(Sb+np.eye(Sb.shape[0])*0.1)
#Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))[0] #Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.5))[0]
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.1)[0] Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.9)[0]
return (-1 / self.sigma2) * np.trace(Sb_inv_N.dot(Sw)) return (-1 / self.sigma2) * np.trace(Sb_inv_N.dot(Sw))
# This function calculates derivative of the log of prior function # This function calculates derivative of the log of prior function
@ -933,8 +933,8 @@ class DGPLVM_Lamda(Prior, Parameterized):
# Calculating inverse of Sb and its transpose and minus # Calculating inverse of Sb and its transpose and minus
# Sb_inv_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1)) # Sb_inv_N = np.linalg.inv(Sb + np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))
#Sb_inv_N = np.linalg.inv(Sb+np.eye(Sb.shape[0])*0.1) #Sb_inv_N = np.linalg.inv(Sb+np.eye(Sb.shape[0])*0.1)
#Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.1))[0] #Sb_inv_N = pdinv(Sb+ np.eye(Sb.shape[0]) * (np.diag(Sb).min() * 0.5))[0]
Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.1)[0] Sb_inv_N = pdinv(Sb + np.eye(Sb.shape[0])*0.9)[0]
Sb_inv_N_trans = np.transpose(Sb_inv_N) Sb_inv_N_trans = np.transpose(Sb_inv_N)
Sb_inv_N_trans_minus = -1 * Sb_inv_N_trans Sb_inv_N_trans_minus = -1 * Sb_inv_N_trans
Sw_trans = np.transpose(Sw) Sw_trans = np.transpose(Sw)

4
GPy/core/sparse_gp.py
View file

@ -137,7 +137,9 @@ class SparseGP(GP):
else: else:
Kxx = kern.Kdiag(Xnew) Kxx = kern.Kdiag(Xnew)
if self.posterior.woodbury_inv.ndim == 2: if self.posterior.woodbury_inv.ndim == 2:
var = Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0) var = (Kxx - np.sum(np.dot(np.atleast_3d(self.posterior.woodbury_inv).T, Kx) * Kx[None,:,:], 1)).T
#For plot_latent, the below code doesn't work!
#var = Kxx - np.sum(np.dot(self.posterior.woodbury_inv.T, Kx) * Kx, 0)
elif self.posterior.woodbury_inv.ndim == 3: elif self.posterior.woodbury_inv.ndim == 3:
var = np.empty((Kxx.shape[0],self.posterior.woodbury_inv.shape[2])) var = np.empty((Kxx.shape[0],self.posterior.woodbury_inv.shape[2]))
for i in range(var.shape[1]): for i in range(var.shape[1]):