From dd059502ace4074bc442046a88f08b8e965841fc Mon Sep 17 00:00:00 2001 From: Mike Croucher Date: Wed, 12 Aug 2015 08:37:13 +0100 Subject: [PATCH] Python 3 fixes --- GPy/core/parameterization/priors.py | 4 ++-- GPy/likelihoods/likelihood.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/GPy/core/parameterization/priors.py b/GPy/core/parameterization/priors.py index 83c83dfd..239b2a26 100644 --- a/GPy/core/parameterization/priors.py +++ b/GPy/core/parameterization/priors.py @@ -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])*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])*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)) # 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])*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])*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_minus = -1 * Sb_inv_N_trans Sw_trans = np.transpose(Sw) diff --git a/GPy/likelihoods/likelihood.py b/GPy/likelihoods/likelihood.py index 67a6a3a3..e961dd1e 100644 --- a/GPy/likelihoods/likelihood.py +++ b/GPy/likelihoods/likelihood.py @@ -607,7 +607,7 @@ class Likelihood(Parameterized): pred_mean = self.predictive_mean(mu, var, Y_metadata=Y_metadata) pred_var = self.predictive_variance(mu, var, pred_mean, Y_metadata=Y_metadata) 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 Ny_samp = 1 s = np.random.randn(mu.shape[0], Nf_samp)*np.sqrt(var) + mu