diff --git a/GPy/inference/latent_function_inference/expectation_propagation_dtc.py b/GPy/inference/latent_function_inference/expectation_propagation_dtc.py index 35b1b7dc..0f972a84 100644 --- a/GPy/inference/latent_function_inference/expectation_propagation_dtc.py +++ b/GPy/inference/latent_function_inference/expectation_propagation_dtc.py @@ -179,7 +179,7 @@ class EPDTC(LatentFunctionInference): if VVT_factor.shape[1] == Y.shape[1]: woodbury_vector = Cpsi1Vf # == Cpsi1V else: - print 'foobar' + print('foobar') psi1V = np.dot(mu_tilde[:,None].T*beta, psi1).T tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0) tmp, _ = dpotrs(LB, tmp, lower=1) diff --git a/GPy/inference/latent_function_inference/var_dtc.py b/GPy/inference/latent_function_inference/var_dtc.py index d61e7f0f..db59df14 100644 --- a/GPy/inference/latent_function_inference/var_dtc.py +++ b/GPy/inference/latent_function_inference/var_dtc.py @@ -170,7 +170,7 @@ class VarDTC(LatentFunctionInference): if VVT_factor.shape[1] == Y.shape[1]: woodbury_vector = Cpsi1Vf # == Cpsi1V else: - print 'foobar' + print('foobar') import ipdb; ipdb.set_trace() psi1V = np.dot(Y.T*beta, psi1).T tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0) diff --git a/GPy/inference/mcmc/samplers.py b/GPy/inference/mcmc/samplers.py index 444d99d7..ff396a96 100644 --- a/GPy/inference/mcmc/samplers.py +++ b/GPy/inference/mcmc/samplers.py @@ -40,7 +40,7 @@ class Metropolis_Hastings: fcurrent = self.model.log_likelihood() + self.model.log_prior() accepted = np.zeros(Ntotal,dtype=np.bool) for it in range(Ntotal): - print "sample %d of %d\r"%(it,Ntotal), + print("sample %d of %d\r"%(it,Ntotal), end=' ') sys.stdout.flush() prop = np.random.multivariate_normal(current, self.cov*self.scale*self.scale) self.model._set_params_transformed(prop) diff --git a/GPy/inference/optimization/conjugate_gradient_descent.py b/GPy/inference/optimization/conjugate_gradient_descent.py index dfc4a48d..274de784 100644 --- a/GPy/inference/optimization/conjugate_gradient_descent.py +++ b/GPy/inference/optimization/conjugate_gradient_descent.py @@ -74,7 +74,7 @@ class _Async_Optimization(Thread): if self.outq is not None: self.outq.put(self.SENTINEL) if self.messages: - print "" + print("") self.runsignal.clear() def run(self, *args, **kwargs): @@ -213,7 +213,7 @@ class Async_Optimize(object): # # print "^C" # self.runsignal.clear() # c.join() - print "WARNING: callback still running, optimisation done!" + print("WARNING: callback still running, optimisation done!") return p.result class CGD(Async_Optimize): diff --git a/GPy/inference/optimization/optimization.py b/GPy/inference/optimization/optimization.py index aa9be793..0d6887e5 100644 --- a/GPy/inference/optimization/optimization.py +++ b/GPy/inference/optimization/optimization.py @@ -125,9 +125,9 @@ class opt_lbfgsb(Optimizer): opt_dict = {} if self.xtol is not None: - print "WARNING: l-bfgs-b doesn't have an xtol arg, so I'm going to ignore it" + print("WARNING: l-bfgs-b doesn't have an xtol arg, so I'm going to ignore it") if self.ftol is not None: - print "WARNING: l-bfgs-b doesn't have an ftol arg, so I'm going to ignore it" + print("WARNING: l-bfgs-b doesn't have an ftol arg, so I'm going to ignore it") if self.gtol is not None: opt_dict['pgtol'] = self.gtol if self.bfgs_factor is not None: @@ -158,7 +158,7 @@ class opt_simplex(Optimizer): if self.ftol is not None: opt_dict['ftol'] = self.ftol if self.gtol is not None: - print "WARNING: simplex doesn't have an gtol arg, so I'm going to ignore it" + print("WARNING: simplex doesn't have an gtol arg, so I'm going to ignore it") opt_result = optimize.fmin(f, self.x_init, (), disp=self.messages, maxfun=self.max_f_eval, full_output=True, **opt_dict) @@ -186,11 +186,11 @@ class opt_rasm(Optimizer): opt_dict = {} if self.xtol is not None: - print "WARNING: minimize doesn't have an xtol arg, so I'm going to ignore it" + print("WARNING: minimize doesn't have an xtol arg, so I'm going to ignore it") if self.ftol is not None: - print "WARNING: minimize doesn't have an ftol arg, so I'm going to ignore it" + print("WARNING: minimize doesn't have an ftol arg, so I'm going to ignore it") if self.gtol is not None: - print "WARNING: minimize doesn't have an gtol arg, so I'm going to ignore it" + print("WARNING: minimize doesn't have an gtol arg, so I'm going to ignore it") opt_result = rasm.minimize(self.x_init, f_fp, (), messages=self.messages, maxnumfuneval=self.max_f_eval) diff --git a/GPy/inference/optimization/scg.py b/GPy/inference/optimization/scg.py index 34dd181f..8960de1d 100644 --- a/GPy/inference/optimization/scg.py +++ b/GPy/inference/optimization/scg.py @@ -21,14 +21,13 @@ # OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. - +from __future__ import print_function import numpy as np import sys - def print_out(len_maxiters, fnow, current_grad, beta, iteration): - print '\r', - print '{0:>0{mi}g} {1:> 12e} {2:< 12.6e} {3:> 12e}'.format(iteration, float(fnow), float(beta), float(current_grad), mi=len_maxiters), # print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r', + print('\r', end=' ') + print('{0:>0{mi}g} {1:> 12e} {2:< 12.6e} {3:> 12e}'.format(iteration, float(fnow), float(beta), float(current_grad), mi=len_maxiters), end=' ') # print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r', sys.stdout.flush() def exponents(fnow, current_grad): @@ -80,7 +79,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=np.inf, display=True, len_maxiters = len(str(maxiters)) if display: - print ' {0:{mi}s} {1:11s} {2:11s} {3:11s}'.format("I", "F", "Scale", "|g|", mi=len_maxiters) + print(' {0:{mi}s} {1:11s} {2:11s} {3:11s}'.format("I", "F", "Scale", "|g|", mi=len_maxiters)) exps = exponents(fnow, current_grad) p_iter = iteration @@ -140,7 +139,7 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=np.inf, display=True, b = np.any(n_exps < exps) if a or b: p_iter = iteration - print '' + print('') if b: exps = n_exps @@ -189,6 +188,6 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=np.inf, display=True, if display: print_out(len_maxiters, fnow, current_grad, beta, iteration) - print "" - print status + print("") + print(status) return x, flog, function_eval, status