def bench_orange(X, y, T, valid):
#
#       .. Orange ..
#
   import orange
   start = datetime.now()

   # prepare data in Orange's format
   columns = []
   for i in range(0,X.shape[1]):
       columns.append("a" +str(i))
   [orange.EnumVariable(x) for x in columns]
   classValues = ['0','1']

   domain = orange.Domain(map(orange.FloatVariable, columns),
                  orange.EnumVariable("class", values=classValues))
   y.shape = (len(y),1) #reshape for Orange
   y[np.where( y < 0)] = 0 # change class labels to 0..K
   orng_train_data = orange.ExampleTable(domain, np.hstack((X,y)))

   valid.shape = (len(valid),1)  #reshape for Orange
   valid[np.where( valid < 0)] = 0 # change class labels to 0..K
   orng_test_data = orange.ExampleTable(domain, np.hstack((T,valid)))

   learner = orange.SVMLearner(orng_train_data, svm_type=orange.SVMLearner.Nu_SVC, kernel_type=orange.SVMLearner.RBF,C=1., gamma= 1./sigma )

   pred = np.empty(T.shape[0], dtype=np.int32)
   for i,e in enumerate(orng_test_data):
       pred[i] = learner(e)

   score = np.mean(pred == valid)
   return score, datetime.now() - start