added two dimensional data analysis support

-added kmeansMkI_2d
-added calcdiff2d to dmlib
-added plzGenNS and ageGenNS  to dmtest to generate unshuffled testdata for kmeans 2d

src/algorithms/dmlib.py

@@ -1,25 +1,34 @@
-# Calculate the difference between two points giving the indexes of these data entries
-def calcdiff(point1, point2):
-	if int(point2) > int(point1):
-		difference = int(point2) - int(point1)
-	else:
-		difference = int(point1) - int(point2)
-	# print("Datapoint: " + str(data[point1]) + " | Cluster: " + str(data[point2]) + " | Difference: " + str(difference))
-	return betrag(difference)
+# Calculate the difference between two points giving the indexes of these xdata entries
+import math
+def calcdiff(point1, point2, data):
+    if int(point2) > int(point1):
+        difference = int(point2) - int(point1)
+    else:
+        difference = int(point1) - int(point2)
+    # print("Datapoint: " + str(xdata[point1]) + " | Cluster: " + str(xdata[point2]) + " | Difference: " + str(difference))
+    return betrag(difference)
+
+def calcdiff2d(point1, point2):
+    point1 = [int(i) for i in point1]
+    point2 = [int(i) for i in point2]
+    difference = math.sqrt(((point2[0])-(point1[0]))**2+((point2[0])-(point1[0]))**2)
+    return betrag(difference)
+
 
 # Get the absolute value of a number and returns it as int
 def betrag(number):
-	if number < 0:
-		number = int((-2 * number) / 2)
-	return number
+    if number < 0:
+        number = int((-2 * number) / 2)
+    return number
+
 
 # Determine the highest int value in an array and returns is as an int
 def findHighest(data):
-	maximum = 0
-	for i in range(0, len(data)):
-		if int(data[i]) > maximum:
-			maximum = int(data[i])
-	return maximum
+    maximum = 0
+    for i in range(0, len(data)):
+        if int(data[i]) > maximum:
+            maximum = int(data[i])
+    return maximum
 
 def pp_calcdiff(data, clusterpoint):
 	max_diff = 0
@@ -37,4 +46,4 @@ def pp_calcdiff_2(data, clusterpoint, clusterpoint_2):
 		if calcdiff(data[item], clusterpoint) + calcdiff(data[item], clusterpoint_2) > max_diff:
 			max_diff = calcdiff(data[item], clusterpoint)
 			new_cluster = data[item]
-	return new_cluster
+	return new_cluster