From b47f9a1b06a26fc2ac721663b7207a8a9b2853b8 Mon Sep 17 00:00:00 2001 From: Ricardo Date: Wed, 18 Sep 2013 16:04:49 +0100 Subject: [PATCH] disp=False in scipy optimizations --- GPy/likelihoods/noise_models/noise_distributions.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/GPy/likelihoods/noise_models/noise_distributions.py b/GPy/likelihoods/noise_models/noise_distributions.py index 9d4eedfb..4fd9c97f 100644 --- a/GPy/likelihoods/noise_models/noise_distributions.py +++ b/GPy/likelihoods/noise_models/noise_distributions.py @@ -107,7 +107,7 @@ class NoiseDistribution(object): :param mu: cavity distribution mean :param sigma: cavity distribution standard deviation """ - return sp.optimize.fmin_ncg(self._nlog_product_scaled,x0=mu,fprime=self._dnlog_product_dgp,fhess=self._d2nlog_product_dgp2,args=(obs,mu,sigma)) + return sp.optimize.fmin_ncg(self._nlog_product_scaled,x0=mu,fprime=self._dnlog_product_dgp,fhess=self._d2nlog_product_dgp2,args=(obs,mu,sigma),disp=False) def _moments_match_analytical(self,obs,tau,v): """ @@ -244,7 +244,7 @@ class NoiseDistribution(object): :param mu: cavity distribution mean :param sigma: cavity distribution standard deviation """ - maximum = sp.optimize.fmin_ncg(self._nlog_conditional_mean_scaled,x0=self._mean(mu),fprime=self._dnlog_conditional_mean_dgp,fhess=self._d2nlog_conditional_mean_dgp2,args=(mu,sigma)) + maximum = sp.optimize.fmin_ncg(self._nlog_conditional_mean_scaled,x0=self._mean(mu),fprime=self._dnlog_conditional_mean_dgp,fhess=self._d2nlog_conditional_mean_dgp2,args=(mu,sigma),disp=False) mean = np.exp(-self._nlog_conditional_mean_scaled(maximum,mu,sigma))/(np.sqrt(self._d2nlog_conditional_mean_dgp2(maximum,mu,sigma))*sigma) """ @@ -267,7 +267,7 @@ class NoiseDistribution(object): :param mu: cavity distribution mean :param sigma: cavity distribution standard deviation """ - maximum = sp.optimize.fmin_ncg(self._nlog_exp_conditional_mean_sq_scaled,x0=self._mean(mu),fprime=self._dnlog_exp_conditional_mean_sq_dgp,fhess=self._d2nlog_exp_conditional_mean_sq_dgp2,args=(mu,sigma)) + maximum = sp.optimize.fmin_ncg(self._nlog_exp_conditional_mean_sq_scaled,x0=self._mean(mu),fprime=self._dnlog_exp_conditional_mean_sq_dgp,fhess=self._d2nlog_exp_conditional_mean_sq_dgp2,args=(mu,sigma),disp=False) mean_squared = np.exp(-self._nlog_exp_conditional_mean_sq_scaled(maximum,mu,sigma))/(np.sqrt(self._d2nlog_exp_conditional_mean_sq_dgp2(maximum,mu,sigma))*sigma) return mean_squared @@ -280,7 +280,7 @@ class NoiseDistribution(object): :predictive_mean: output's predictive mean, if None _predictive_mean function will be called. """ # E( V(Y_star|f_star) ) - maximum = sp.optimize.fmin_ncg(self._nlog_exp_conditional_variance_scaled,x0=self._variance(mu),fprime=self._dnlog_exp_conditional_variance_dgp,fhess=self._d2nlog_exp_conditional_variance_dgp2,args=(mu,sigma)) + maximum = sp.optimize.fmin_ncg(self._nlog_exp_conditional_variance_scaled,x0=self._variance(mu),fprime=self._dnlog_exp_conditional_variance_dgp,fhess=self._d2nlog_exp_conditional_variance_dgp2,args=(mu,sigma),disp=False) exp_var = np.exp(-self._nlog_exp_conditional_variance_scaled(maximum,mu,sigma))/(np.sqrt(self._d2nlog_exp_conditional_variance_dgp2(maximum,mu,sigma))*sigma) """ @@ -357,7 +357,7 @@ class NoiseDistribution(object): :param mu: latent variable's predictive mean :param sigma: latent variable's predictive standard deviation """ - return sp.optimize.fmin_ncg(self._nlog_joint_predictive_scaled,x0=(mu,self.gp_link.transf(mu)),fprime=self._gradient_nlog_joint_predictive,fhess=self._hessian_nlog_joint_predictive,args=(mu,sigma)) + return sp.optimize.fmin_ncg(self._nlog_joint_predictive_scaled,x0=(mu,self.gp_link.transf(mu)),fprime=self._gradient_nlog_joint_predictive,fhess=self._hessian_nlog_joint_predictive,args=(mu,sigma),disp=False) def predictive_values(self,mu,var): """