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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 08:11:11 +00:00
parent 2ca24a88f5
commit 1521b3e260
10 changed files with 37 additions and 37 deletions
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18
GPy/core/model.py
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@ -82,7 +82,7 @@ class Model(Parameterized):
pool.close() # signal that no more data coming in
pool.join() # wait for all the tasks to complete
except KeyboardInterrupt:
print "Ctrl+c received, terminating and joining pool."
print("Ctrl+c received, terminating and joining pool.")
pool.terminate()
pool.join()
@ -95,10 +95,10 @@ class Model(Parameterized):
self.optimization_runs.append(jobs[i].get())
if verbose:
print("Optimization restart {0}/{1}, f = {2}".format(i + 1, num_restarts, self.optimization_runs[-1].f_opt))
print(("Optimization restart {0}/{1}, f = {2}".format(i + 1, num_restarts, self.optimization_runs[-1].f_opt)))
except Exception as e:
if robust:
print("Warning - optimization restart {0}/{1} failed".format(i + 1, num_restarts))
print(("Warning - optimization restart {0}/{1} failed".format(i + 1, num_restarts)))
else:
raise e
@ -237,10 +237,10 @@ class Model(Parameterized):
"""
if self.is_fixed or self.size == 0:
print 'nothing to optimize'
print('nothing to optimize')
if not self.update_model():
print "updates were off, setting updates on again"
print("updates were off, setting updates on again")
self.update_model(True)
if start == None:
@ -305,7 +305,7 @@ class Model(Parameterized):
transformed_index = (indices - (~self._fixes_).cumsum())[transformed_index[which[0]]]
if transformed_index.size == 0:
print "No free parameters to check"
print("No free parameters to check")
return
# just check the global ratio
@ -342,7 +342,7 @@ class Model(Parameterized):
header_string = ["{h:^{col}}".format(h=header[i], col=cols[i]) for i in range(len(cols))]
header_string = map(lambda x: '|'.join(x), [header_string])
separator = '-' * len(header_string[0])
print '\n'.join([header_string[0], separator])
print('\n'.join([header_string[0], separator]))
if target_param is None:
param_index = range(len(x))
transformed_index = param_index
@ -358,7 +358,7 @@ class Model(Parameterized):
transformed_index = param_index
if param_index.size == 0:
print "No free parameters to check"
print("No free parameters to check")
return
gradient = self._grads(x).copy()
@ -392,7 +392,7 @@ class Model(Parameterized):
ng = '%.6f' % float(numerical_gradient)
df = '%1.e' % float(df_ratio)
grad_string = "{0:<{c0}}|{1:^{c1}}|{2:^{c2}}|{3:^{c3}}|{4:^{c4}}|{5:^{c5}}".format(formatted_name, r, d, g, ng, df, c0=cols[0] + 9, c1=cols[1], c2=cols[2], c3=cols[3], c4=cols[4], c5=cols[5])
print grad_string
print(grad_string)
self.optimizer_array = x
return ret