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Convert print to function for Python 3 compatibility. This breaks compatibility for versions of Python < 2.6

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This commit is contained in:
Mike Croucher 2015-02-26 08:48:48 +00:00
parent 906f69e20e
commit 5601a580de
6 changed files with 18 additions and 19 deletions
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2
GPy/inference/latent_function_inference/expectation_propagation_dtc.py
View file

@ -179,7 +179,7 @@ class EPDTC(LatentFunctionInference):
if VVT_factor.shape[1] == Y.shape[1]: if VVT_factor.shape[1] == Y.shape[1]:
woodbury_vector = Cpsi1Vf # == Cpsi1V woodbury_vector = Cpsi1Vf # == Cpsi1V
else: else:
print 'foobar' print('foobar')
psi1V = np.dot(mu_tilde[:,None].T*beta, psi1).T psi1V = np.dot(mu_tilde[:,None].T*beta, psi1).T
tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0) tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)
tmp, _ = dpotrs(LB, tmp, lower=1) tmp, _ = dpotrs(LB, tmp, lower=1)

2
GPy/inference/latent_function_inference/var_dtc.py
View file

@ -170,7 +170,7 @@ class VarDTC(LatentFunctionInference):
if VVT_factor.shape[1] == Y.shape[1]: if VVT_factor.shape[1] == Y.shape[1]:
woodbury_vector = Cpsi1Vf # == Cpsi1V woodbury_vector = Cpsi1Vf # == Cpsi1V
else: else:
print 'foobar' print('foobar')
import ipdb; ipdb.set_trace() import ipdb; ipdb.set_trace()
psi1V = np.dot(Y.T*beta, psi1).T psi1V = np.dot(Y.T*beta, psi1).T
tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0) tmp, _ = dtrtrs(Lm, psi1V, lower=1, trans=0)

2
GPy/inference/mcmc/samplers.py
View file

@ -40,7 +40,7 @@ class Metropolis_Hastings:
fcurrent = self.model.log_likelihood() + self.model.log_prior() fcurrent = self.model.log_likelihood() + self.model.log_prior()
accepted = np.zeros(Ntotal,dtype=np.bool) accepted = np.zeros(Ntotal,dtype=np.bool)
for it in range(Ntotal): 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() sys.stdout.flush()
prop = np.random.multivariate_normal(current, self.cov*self.scale*self.scale) prop = np.random.multivariate_normal(current, self.cov*self.scale*self.scale)
self.model._set_params_transformed(prop) self.model._set_params_transformed(prop)

4
GPy/inference/optimization/conjugate_gradient_descent.py
View file

@ -74,7 +74,7 @@ class _Async_Optimization(Thread):
if self.outq is not None: if self.outq is not None:
self.outq.put(self.SENTINEL) self.outq.put(self.SENTINEL)
if self.messages: if self.messages:
print "" print("")
self.runsignal.clear() self.runsignal.clear()
def run(self, *args, **kwargs): def run(self, *args, **kwargs):
@ -213,7 +213,7 @@ class Async_Optimize(object):
# # print "^C" # # print "^C"
# self.runsignal.clear() # self.runsignal.clear()
# c.join() # c.join()
print "WARNING: callback still running, optimisation done!" print("WARNING: callback still running, optimisation done!")
return p.result return p.result
class CGD(Async_Optimize): class CGD(Async_Optimize):

12
GPy/inference/optimization/optimization.py
View file

@ -125,9 +125,9 @@ class opt_lbfgsb(Optimizer):
opt_dict = {} opt_dict = {}
if self.xtol is not None: 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: 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: if self.gtol is not None:
opt_dict['pgtol'] = self.gtol opt_dict['pgtol'] = self.gtol
if self.bfgs_factor is not None: if self.bfgs_factor is not None:
@ -158,7 +158,7 @@ class opt_simplex(Optimizer):
if self.ftol is not None: if self.ftol is not None:
opt_dict['ftol'] = self.ftol opt_dict['ftol'] = self.ftol
if self.gtol is not None: 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, opt_result = optimize.fmin(f, self.x_init, (), disp=self.messages,
maxfun=self.max_f_eval, full_output=True, **opt_dict) maxfun=self.max_f_eval, full_output=True, **opt_dict)
@ -186,11 +186,11 @@ class opt_rasm(Optimizer):
opt_dict = {} opt_dict = {}
if self.xtol is not None: 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: 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: 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, opt_result = rasm.minimize(self.x_init, f_fp, (), messages=self.messages,
maxnumfuneval=self.max_f_eval) maxnumfuneval=self.max_f_eval)

15
GPy/inference/optimization/scg.py
View file

@ -21,14 +21,13 @@
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE # OF THIS SOFTWARE, EVEN IF ADVISED OF THE
# POSSIBILITY OF SUCH DAMAGE. # POSSIBILITY OF SUCH DAMAGE.
from __future__ import print_function
import numpy as np import numpy as np
import sys import sys
def print_out(len_maxiters, fnow, current_grad, beta, iteration): def print_out(len_maxiters, fnow, current_grad, beta, iteration):
print '\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), # print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\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), end=' ') # print 'Iteration:', iteration, ' Objective:', fnow, ' Scale:', beta, '\r',
sys.stdout.flush() sys.stdout.flush()
def exponents(fnow, current_grad): 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)) len_maxiters = len(str(maxiters))
if display: 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) exps = exponents(fnow, current_grad)
p_iter = iteration 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) b = np.any(n_exps < exps)
if a or b: if a or b:
p_iter = iteration p_iter = iteration
print '' print('')
if b: if b:
exps = n_exps exps = n_exps
@ -189,6 +188,6 @@ def SCG(f, gradf, x, optargs=(), maxiters=500, max_f_eval=np.inf, display=True,
if display: if display:
print_out(len_maxiters, fnow, current_grad, beta, iteration) print_out(len_maxiters, fnow, current_grad, beta, iteration)
print "" print("")
print status print(status)
return x, flog, function_eval, status return x, flog, function_eval, status