Addressed FSTEPS for cases with <=10 input features

sklearn_training.py

@@ -179,6 +179,9 @@ for percentage in feature_ranges:
     k = np.ceil((nfeat * percentage) / 100).astype(np.int)
     FSTEPS.append(k)
 
+# address cases with <=10 features yielding duplicated entries
+FSTEPS = np.unique(np.array(FSTEPS)).tolist()
+
 # prepare output files
 stabilityf = open(OUTFILE + "_stability.txt", 'w')
 stability_w = csv.writer(stabilityf, delimiter='\t', lineterminator='\n')