diff --git a/tools/karoo_iris_plot.py b/tools/karoo_iris_plot.py
index 5847850..b22ba49 100644
--- a/tools/karoo_iris_plot.py
+++ b/tools/karoo_iris_plot.py
@@ -1,6 +1,6 @@
 # Karoo Iris Plot
 # by Kai Staats, MSc UCT / AIMS and Arun Kumar, PhD
-# version 0.9.2.0
+# version 0.9.2.1
 
 import sys
 import numpy as np
diff --git a/tools/karoo_multiclassifier.py b/tools/karoo_multiclassifier.py
index 92502a4..ae470ce 100644
--- a/tools/karoo_multiclassifier.py
+++ b/tools/karoo_multiclassifier.py
@@ -1,6 +1,6 @@
 # Karoo Multiclass Classifer Test
 # by Kai Staats, MSc UCT / AIMS
-# version 0.9.2.0
+# version 0.9.2.1
 
 '''
 This is a toy script, designed to allow you to play with multiclass classification using the same underlying function
@@ -20,7 +20,7 @@ while True:
 n = range(1,100)
 while True:
 	try:
-		class_labels = raw_input('\t Enter the number of class labels (default 4): ')
+		class_labels = raw_input('\t Enter the number of class labels / solutions (default 4): ')
 		if class_labels not in str(n) and class_labels not in '': raise ValueError()
 		if class_labels == '0': class_labels = 1; break
 		class_labels = class_labels or 4; class_labels = int(class_labels); break
@@ -32,36 +32,36 @@ min_val = 0 - skew - 1 # add a data point to the left
 if class_labels & 1: max_val = 0 + skew + 3 # add a data point to the right if odd number of class labels
 else: max_val = 0 + skew + 2 # add a data point to the right if even number of class labels
 
-print '\n\t class_labels =', range(class_labels)
-print '\t solutions = [', min_val, '...', max_val,']'
+print '\n\t solutions =', range(class_labels)
+print '\t results = [', min_val, '...', max_val,']'
 print '\t skew =', skew, '\n'
 
 if class_type == 'i':
-	for solution in arange(min_val, max_val, 0.5):
-		for label in range(class_labels):
+	for result in arange(min_val, max_val, 0.5):
+		for solution in range(class_labels):
 		
-			if label == 0 and solution <= 0 - skew: # check for the first class
-				fitness = 1; print '\t\033[36m\033[1m class', label, '\033[0;0m\033[36mas\033[1m', solution, '\033[0;0m\033[36m<=', 0 - skew, '\033[0;0m'
+			if solution == 0 and result <= 0 - skew: # check for the first class
+				fitness = 1; print '\t\033[36m\033[1m class', solution, '\033[0;0m\033[36mas\033[1m', result, '\033[0;0m\033[36m<=', 0 - skew, '\033[0;0m'
 				
-			elif label == class_labels - 1 and solution > label - 1 - skew: # check for the last class
-				fitness = 1; print '\t\033[36m\033[1m class', label, '\033[0;0m\033[36mas\033[1m', solution, '\033[0;0m\033[36m>', label - 1 - skew, '\033[0;0m'
+			elif solution == class_labels - 1 and result > solution - 1 - skew: # check for the last class
+				fitness = 1; print '\t\033[36m\033[1m class', solution, '\033[0;0m\033[36mas\033[1m', result, '\033[0;0m\033[36m>', solution - 1 - skew, '\033[0;0m'
 				
-			elif label - 1 - skew < solution <= label - skew: # check for class bins between first and last
-				fitness = 1; print '\t\033[36m\033[1m class', label, '\033[0;0m\033[36mas', label - 1 - skew, '<\033[1m', solution, '\033[0;0m\033[36m<=', label - skew, '\033[0;0m'
+			elif solution - 1 - skew < result <= solution - skew: # check for class bins between first and last
+				fitness = 1; print '\t\033[36m\033[1m class', solution, '\033[0;0m\033[36mas', solution - 1 - skew, '<\033[1m', result, '\033[0;0m\033[36m<=', solution - skew, '\033[0;0m'
 				
-			else: fitness = 0 #; print '\t\033[36m no match for', solution, 'in class', label, '\033[0;0m' # no class match
+			else: fitness = 0 #; print '\t\033[36m no match for', result, 'in class', solution, '\033[0;0m' # no class match
 			
 		# print ''
 
 
 if class_type == 'f':
-	for solution in arange(min_val, max_val, .5):
-		for label in range(class_labels):
+	for result in arange(min_val, max_val, .5):
+		for solution in range(class_labels):
 		
-			if label - 1 - skew < solution <= label - skew: # check for discrete, finite class bins
-				fitness = 1; print '\t\033[36m\033[1m class', label, '\033[0;0m\033[36mas', label - 1 - skew, '<\033[1m', solution, '\033[0;0m\033[36m<=', label - skew, '\033[0;0m'
+			if solution - 1 - skew < result <= solution - skew: # check for discrete, finite class bins
+				fitness = 1; print '\t\033[36m\033[1m class', solution, '\033[0;0m\033[36mas', solution - 1 - skew, '<\033[1m', result, '\033[0;0m\033[36m<=', solution - skew, '\033[0;0m'
 				
-			else: fitness = 0 #; print '\t\033[36m no match for', solution, 'in class', label, '\033[0;0m' # no class match
+			else: fitness = 0 #; print '\t\033[36m no match for', result, 'in class', solution, '\033[0;0m' # no class match
 			
 		# print ''
 
diff --git a/tools/karoo_normalise.py b/tools/karoo_normalise.py
index f21c11f..4ce473f 100644
--- a/tools/karoo_normalise.py
+++ b/tools/karoo_normalise.py
@@ -1,6 +1,6 @@
 # Karoo Data Normalisation
 # by Kai Staats, MSc UCT
-# version 0.9.2.0
+# version 0.9.2.1
 
 import sys
 import numpy as np
diff --git a/tools/karoo_sort.py b/tools/karoo_sort.py
index 145f32d..a1b3826 100644
--- a/tools/karoo_sort.py
+++ b/tools/karoo_sort.py
@@ -1,6 +1,6 @@
 # Karoo Dataset Builder
 # by Kai Staats, MSc UCT / AIMS and Arun Kumar, PhD
-# version 0.9.2.0
+# version 0.9.2.1
 
 import sys
 import numpy as np