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Python 3 fixes

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This commit is contained in:
Mike Croucher 2015-08-12 08:37:13 +01:00
parent 28c03148ec
commit dd059502ac
2 changed files with 3 additions and 3 deletions
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4
GPy/core/parameterization/priors.py
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@ -903,7 +903,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
# 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() * 0.5))[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])*0.9)[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
@ -927,7 +927,7 @@ class DGPLVM_Lamda(Prior, Parameterized):
# 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() * 0.5))[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])*0.9)[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)

2
GPy/likelihoods/likelihood.py
View file

@ -607,7 +607,7 @@ class Likelihood(Parameterized):
pred_mean = self.predictive_mean(mu, var, Y_metadata=Y_metadata) pred_mean = self.predictive_mean(mu, var, Y_metadata=Y_metadata)
pred_var = self.predictive_variance(mu, var, pred_mean, Y_metadata=Y_metadata) pred_var = self.predictive_variance(mu, var, pred_mean, Y_metadata=Y_metadata)
except NotImplementedError: except NotImplementedError:
print "Finding predictive mean and variance via sampling rather than quadrature" print("Finding predictive mean and variance via sampling rather than quadrature")
Nf_samp = 300 Nf_samp = 300
Ny_samp = 1 Ny_samp = 1
s = np.random.randn(mu.shape[0], Nf_samp)*np.sqrt(var) + mu s = np.random.randn(mu.shape[0], Nf_samp)*np.sqrt(var) + mu