fix.

E_MOA_2P.py

@@ -29,14 +29,17 @@
 
 res = np.load('results/moa_clf.npy') # measures, datasets, reps, folds, clfs
 
-res_mean = np.mean(res, axis=2)
-print(res_mean.shape)
+print(res.shape)
+res = res.reshape(9,4,3,10,5)
 
-fig, ax = plt.subplots(4, 3, figsize=(10,20), sharex=True, sharey=True)
+res_mean = np.mean(res, axis=(2,3))
+
+
+fig, ax = plt.subplots(2, 2, figsize=(8,10), sharex=True, sharey=True)
 ax=ax.ravel()
 plt.suptitle('MOA', fontsize=18, y=0.99)
 
-for dataset_id, dataset in enumerate(streams):
+for dataset_id, dataset in enumerate(['RBF', 'LED', 'HYPERPLANE', 'SEA']):
 
     axx = ax[dataset_id]
 

E_MOA_5.py

@@ -13,11 +13,11 @@
     res = np.load('results/combined_moa_%i.npy' % i)
     res_all.append(res)
 
-res = np.array(res_all).reshape(3,4,119,500)
-print(res.shape) # reps, types, features+label, chunks
+res = np.array(res_all).reshape(4,3,119,500)
+print(res.shape) # types, reps, features+label, chunks
 
 X = res[:,:,indexes]
-print(X.shape)# reps, types, selected features, chunks
+print(X.shape)# types, reps, selected features, chunks
 
 types = ['RBF', 'LED', 'HYPERPLANE', 'SEA']
 
@@ -33,7 +33,7 @@
 
     for rep in range(3):
 
-        X_all = X[rep, t]
+        X_all = X[t, rep]
 
         for a in range(len(labels)):
             X_all[a] -= np.mean(X_all[a])
@@ -69,7 +69,7 @@
         covs = []
         for rep in range(3):
 
-            X_temp = X_w[rep, t]
+            X_temp = X_w[t, rep]
 
             for a in range(len(labels)):
                 X_temp[a] -= np.mean(X_temp[a])