diff --git a/.circleci/config.yml b/.circleci/config.yml
index e43ad0b..08c1cd7 100644
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -11,7 +11,7 @@ orbs:
   # See the orb documentation here: https://circleci.com/developer/orbs/orb/circleci/python
   codecov: codecov/codecov@3.0.0
   slack: circleci/slack@4.4.4
-  python: circleci/python@1.2
+  python: circleci/python@2.1.1
   
 
 # Define a job to be invoked later in a workflow.
@@ -66,7 +66,7 @@ jobs:
 # Invoke jobs via workflows
 # See: https://circleci.com/docs/2.0/configuration-reference/#workflows
 workflows:
-  Python-3.8: # This is the name of the workflow, feel free to change it to better match your workflow.
+  Python-3.10: # This is the name of the workflow, feel free to change it to better match your workflow.
     # Inside the workflow, you define the jobs you want to run.
     jobs:
       - build-and-test:
diff --git a/src/denmune.py b/src/denmune.py
index 7fc86d1..27b3a29 100644
--- a/src/denmune.py
+++ b/src/denmune.py
@@ -125,29 +125,32 @@ def __init__(
         if isinstance(train_data, pd.DataFrame):
             train_data = train_data.to_numpy()
             train_data = train_data.copy(order='C')
-        elif type(train_data == np.ndarray and not train_data.data.c_contiguous):  
+        # elif type(train_data == np.ndarray) and (not train_data.data.c_contiguous):
+        else:    
             train_data = train_data.copy(order='C')
 
         if train_truth is not None:
           if isinstance(train_truth, pd.Series):
               train_truth = train_truth.to_numpy()
               train_truth = train_truth.copy(order='C')
-          elif type(train_truth == np.ndarray and not train_truth.data.c_contiguous):  
+          # elif type(train_truth == np.ndarray) and (not train_truth.data.c_contiguous):  
+          else:  
             train_truth = train_truth.copy(order='C')   
 
         if test_data is not None:
           if isinstance(test_data, pd.DataFrame):
             test_data = test_data.to_numpy()
             test_data = test_data.copy(order='C')
-          elif type(test_data == np.ndarray and not test_data.data.c_contiguous): 
-            print ('I am in two') 
+          # elif type(test_data == np.ndarray) and (not test_data.data.c_contiguous): 
+          else:    
             test_data = test_data.copy(order='C')
     
         if test_truth is not None:
           if isinstance(test_truth, pd.Series):
               test_truth = test_truth.to_numpy()
               test_truth = test_truth.copy(order='C')
-          elif type(test_truth == np.ndarray and not test_truth.data.c_contiguous):  
+          # elif type(test_truth == np.ndarray) and (not test_truth.data.c_contiguous):  
+          else:    
               test_truth = test_truth.copy(order='C')       
 
         self.train_sz = len(train_data)
diff --git a/src/tests/test_denmune.py b/src/tests/test_denmune.py
index afd6d36..c975ceb 100644
--- a/src/tests/test_denmune.py
+++ b/src/tests/test_denmune.py
@@ -46,7 +46,7 @@ def test_parameters(train_data, train_truth, test_data, test_truth, validate, sh
                     labels, validity = dm.fit_predict(validate=validate, show_plots=show_plots, show_noise=show_noise, show_analyzer=show_analyzer)
                     # This test use data that are not perfectly separable so the
                     # accuracy is not 1. Accuracy around 0.70
-                    assert ( np.mean(dm.labels_pred == y_cc) > 0.70 or (1 - np.mean( dm.labels_pred == y_cc)  > 0.70) ) 
+                    assert ( np.mean(labels == y_cc) > 0.70 or (1 - np.mean( labels == y_cc)  > 0.70) ) 
 
 
 def test_DenMune_propagation():
@@ -118,6 +118,6 @@ def test_exceptions():
         #raise Exception('train_data is not None and  train_truth is not None and test_truth is not None  and test_data is None')
     with pytest.raises(Exception) as execinfo:
 
-        dm = DenMune(train_data=train_data, train_truth=train_labels) # default value for k_nearest is 0 which is invalid
+        dm = DenMune(train_data=train_data, train_truth=train_labels, k_nearest=0) # default value for k_nearest is 1 which is valid
         #labels, validity = dm.fit_predict()     
         #raise Exception('k-nearest neighbor should be at least 1')