Merge pull request #1073 from SheffieldML/1056-test-with-python-2-sty…

…le-print-statements

remove python2-styled print statements

CHANGELOG.md

@@ -2,6 +2,8 @@
 
 ## Unreleased
 
+* remove python2-style print statements (#1056)
+
 * update import in `.plotting.matplot_dep.defaults` due to change in matplotlib
 
 * Correct dl_dm term in student t inference #1065

GPy/testing/deactivated/deactivated_test_mpi.py

@@ -23,11 +23,11 @@ def test_BayesianGPLVM_MPI(self):
 m = GPy.models.BayesianGPLVM(data.T,1,mpi_comm=comm)
 m.optimize(max_iters=10)
 if comm.rank==0:
-    print float(m.objective_function())
+    print(float(m.objective_function()))
     m.inference_method.mpi_comm=None
     m.mpi_comm=None
     m._trigger_params_changed()
-    print float(m.objective_function())
+    print(float(m.objective_function()))
             """
             with open("mpi_test__.py", "w") as f:
                 f.write(code)
@@ -59,11 +59,11 @@ def test_SparseGPRegression_MPI(self):
 m = GPy.models.SparseGPRegression(data[:1].T,data[1:2].T,mpi_comm=comm)
 m.optimize(max_iters=10)
 if comm.rank==0:
-    print float(m.objective_function())
+    print(float(m.objective_function()))
     m.inference_method.mpi_comm=None
     m.mpi_comm=None
     m._trigger_params_changed()
-    print float(m.objective_function())
+    print(float(m.objective_function()))
             """
             with open("mpi_test__.py", "w") as f:
                 f.write(code)

GPy/testing/mpi_test__.py

@@ -1,21 +1,20 @@
-
 import numpy as np
 import GPy
 from mpi4py import MPI
+
 np.random.seed(123456)
 comm = MPI.COMM_WORLD
 N = 100
-x = np.linspace(-6., 6., N)
+x = np.linspace(-6.0, 6.0, N)
 y = np.sin(x) + np.random.randn(N) * 0.05
 comm.Bcast(y)
-data = np.vstack([x,y])
-#infr = GPy.inference.latent_function_inference.VarDTC_minibatch(mpi_comm=comm)
-m = GPy.models.SparseGPRegression(data[:1].T,data[1:2].T,mpi_comm=comm)
+data = np.vstack([x, y])
+# infr = GPy.inference.latent_function_inference.VarDTC_minibatch(mpi_comm=comm)
+m = GPy.models.SparseGPRegression(data[:1].T, data[1:2].T, mpi_comm=comm)
 m.optimize(max_iters=10)
-if comm.rank==0:
-    print float(m.objective_function())
-    m.inference_method.mpi_comm=None
-    m.mpi_comm=None
+if comm.rank == 0:
+    print(float(m.objective_function()))
+    m.inference_method.mpi_comm = None
+    m.mpi_comm = None
     m._trigger_params_changed()
-    print float(m.objective_function())
-
+    print(float(m.objective_function()))