--- a/python/indicators.py	Tue Dec 25 02:16:10 2012 -0500
+++ b/python/indicators.py	Tue Dec 25 02:20:25 2012 -0500
@@ -4,7 +4,7 @@
 __metaclass__ = type
 
 import moving
-
+from utils import LCSS
 # need for a class representing the indicators, their units, how to print them in graphs...
 class TemporalIndicator:
     '''Class for temporal indicators
@@ -82,7 +82,28 @@
         plot([x/xfactor for x in time], [self.values[i] for i in time], options+marker, **kwargs)
         if self.maxValue:
             ylim(ymax = self.maxValue)
-
+	
+    def valueSorted(self):
+	''' return the values after sort the keys in the indicator''' 
+        values=[]
+        keys = self.values.keys()
+        keys.sort()
+        for key in keys:
+            values.append(self.values[key]) 
+        return values
+    @staticmethod
+	def getDLCSS(TemporalIndicator1,TemporalIndicator2, threshold, delta= np.inf , method='min' ):
+	''' compute the distance between two indicators using LCSS
+	two common methods are used: min or mean of the indicators length'''
+        l1= TemporalIndicator1.valueSorted
+		l2= TemporalIndicator2.valueSorted
+		if method = 'min':
+			DLCSS= 1- (LCSS(l1,l2, threshold, delta, distance))/min(len(l1),len(l2)))
+		if method = 'mean':
+			average= len(l1)+len(l2))/2
+			DLCSS= 1- ((LCSS(l1,l2, threshold, delta, distance))/average)
+		return DLCSS
+		
 class SeverityIndicator(TemporalIndicator):
     '''Class for severity indicators 
     field mostSevereIsMax is True 

--- a/python/ml.py	Tue Dec 25 02:16:10 2012 -0500
+++ b/python/ml.py	Tue Dec 25 02:20:25 2012 -0500
@@ -1,6 +1,8 @@
 #! /usr/bin/env python
 '''Libraries for machine learning algorithms'''
 
+import numpy as np
+
 __metaclass__ = type
 
 class Centroid:
@@ -55,3 +57,24 @@
             centroids[i].add(instance)
 
     return centroids
+
+def spectralClustering(similarityMatrix,k):	
+	''' Steps of Spectral Clustering'''
+	n= len(similarityMatrix)
+	# create Laplacian matrix
+	rowsum = np.sum(similarityMatrix,axis=0)
+	D = np.diag(1 / np.sqrt(rowsum))
+	I = np.identity(n)
+	L = I - np.dot(D,np.dot(similarityMatrix,D))
+	# compute eigenvectors of L
+	U,sigma,V = np.linalg.svd(L)
+	# create feature vector from k first eigenvectors
+	# by stacking eigenvectors as columns
+	features = np.array(V[:k]).T
+	# k-means
+	from scipy.cluster.vq import kmeans, whiten, vq
+	features = whiten(features)
+	centroids,distortion = kmeans(features,k,iter=20) # default iter = 20
+	code,distance = vq(features,centroids) # code starting from 0 (represent first cluster) to k-1 (last cluster)
+	return code,sigma
+