translate mkl.py from python 2to3

mkl.py

@@ -20,9 +20,9 @@
     from modshogun import MKLClassification
     from modshogun import LibSVM
 except ImportError:
-    print "ImportError:"
-    print "Importing the large-scale machine learning toolbox SHOGUN failed."
-    print "Refer to instructions on http://www.shogun-toolbox.org/ for installing SHOGUN with the python_modular interface."
+    print("ImportError:")
+    print("Importing the large-scale machine learning toolbox SHOGUN failed.")
+    print("Refer to instructions on http://www.shogun-toolbox.org/ for installing SHOGUN with the python_modular interface.")
     raise
 
 
@@ -46,7 +46,7 @@
 parser.add_option("-L", "--testlabelsFilename", type="str", action="store", default = "kernels/testlabels.txt",
                         dest="testlabelsFilename", help="Name of the file providing the testlabels [default: %default]")
 parser.add_option("-I", "--imbalance", type="float", action="store", default = "1.0",
-                        dest="Imbalance", help="Imbalance, multiplied with cost parameter for SVM negative class [default: %default]")
+                        dest="Imbalance", help="Imbalance, multiplied with cost parameter for SVM minority class [default: %default]")
 parser.add_option("-t", "--whetherPerformTest", type="int", action="store", default = 1,
                         dest="performTest", help="Specify 1 if performTest, else 0.[default: %default]")
 
@@ -66,7 +66,7 @@
 trainkernel = CombinedKernel()
 for k in range(0, len(kernelFilenames)):
     thisFname = kernelFilenames[k]
-    print thisFname
+    print(thisFname)
     if os.path.exists(thisFname):
         trainkernel.append_kernel(CustomKernel(numpy.loadtxt(thisFname, delimiter=',')))
     else:
@@ -80,7 +80,7 @@
 mkl.set_mkl_norm(options.MKLNorm) #2,3
 
 # set cost (neg, pos)
-mkl.set_C(options.C*options.Imbalance, options.C)
+mkl.set_C(options.C, options.C*options.Imbalance)
 mkl.set_C_mkl(options.C)
 # set kernel and labels
 mkl.set_kernel(trainkernel)
@@ -94,20 +94,20 @@
 mkl.set_epsilon(1e-5)
 mkl.parallel.set_num_threads(10)
 mkl.io.disable_progress()
-print "Before SVM train with MKL"
+print("Before SVM train with MKL")
 try:
     mkl.train()
 except:
-    print "Train error caught here"
+    print("Train error caught here")
     raise
 
-print "Training done"
+print("Training done")
 alphas = mkl.get_alphas()
 bias = mkl.get_bias()
 sv = mkl.get_support_vectors()
-print str(bias)
+print(str(bias))
 kw = trainkernel.get_subkernel_weights()
-print kw
+print(kw)
 head, tail = os.path.split(options.subkernelWeightsFilename)
 biasFilename = head+'/biasValue.txt'
 alphasFilename = head+'/alphas.txt'
@@ -121,7 +121,7 @@
 numpy.savetxt(svIndicesFilename, sv, fmt='%d', delimiter=',')
 # write the subkernel weights to file
 numpy.savetxt(options.subkernelWeightsFilename, kw, fmt='%.10f', delimiter=',')
-print "Subkernel weights, bias and alphas written to individual files"
+print("Subkernel weights, bias and alphas written to individual files")
 
 
 # separately for test, one will have to 
@@ -146,9 +146,9 @@
     predictions = mkl.apply().get_values()
 
     numpy.savetxt(options.predictedlabelsFilename, predictions, fmt='%.10f', delimiter=',')
-    print "Predicted labels written to file"
+    print("Predicted labels written to file")
 else:
     predictions = mkl.apply().get_values()
     numpy.savetxt(options.predictedlabelsFilename, predictions, fmt='%.10f', delimiter=',')
-    print "Training prediction labels written to file"
+    print("Training prediction labels written to file")