--- a/python/utils.py	Tue May 07 02:02:55 2013 +0200
+++ b/python/utils.py	Tue May 07 18:43:40 2013 +0200
@@ -179,16 +179,49 @@
     based on the threshold on distance between two elements of lists l1, l2
     '''
     from numpy import zeros, int as npint
-    m = len(l1)
-    n = len(l2)
-    similarity = zeros((m+1,n+1), dtype = npint)
-    for i in xrange(1,m+1):
-        for j in xrange(max(1,i-delta),min(n+1,i+delta)):
+    n1 = len(l1)
+    n2 = len(l2)
+    similarity = zeros((n1+1,n2+1), dtype = npint)
+    for i in xrange(1,n1+1):
+        for j in xrange(max(1,i-delta),min(n2+1,i+delta+1)):
             if distance(l1[i-1], l2[j-1])<=threshold:
                 similarity[i][j] = similarity[i-1][j-1]+1
             else:
                 similarity[i][j] = max(similarity[i-1][j], similarity[i][j-1])
-    return similarity[-1][-1]
+    return max(max(similarity[:,-1]), max(similarity[-1,:]))
+
+def normalizedLCSS(l1, l2, threshold, distance, delta = float('inf'), lengthMethod = min):
+    ''' compute the normalized LCSS
+    ie, the LCSS divided by the min or mean of the indicator lengths (using lengthMethod)
+    lengthMethod = lambda x,y:float(x,y)/2'''
+    return float(LCSS(l1, l2, threshold, distance, delta))/lengthMethod(len(l1), len(l2))
+
+def DLCSS(l1, l2, threshold, distance, delta = float('inf'), lengthMethod = min):
+    ''' compute the LCSS distance'''
+    return 1-normalizedLCSS(l1, l2, threshold, distance, delta, lengthMethod)
+
+def alignedLCSS(_l1, _l2, threshold, distance, delta):
+    '''returns the best matching if using a finite delta by shiftinig the series alignments'''
+    if len(_l2) < len(_l1): # l1 is the shortest
+        l1 = _l2
+        l2 = _l1
+    else:
+        l1 = _l1
+        l2 = _l2
+    n1 = len(l1)
+    n2 = len(l2)
+    # for i in xrange(delta, n1+n2-delta): # i is the alignment of the end of l1 in l2
+    #     print l1[min(-i-1,n1):] # min(n1+n2-i,n1)
+    #     print l2[max(0,i-n1):]
+    #     print LCSS(l1[min(-i-1,n1):], l2[max(0,i-n1):], threshold, distance, delta)
+    lcss = [LCSS(l1[min(-i-1,n1):], l2[max(0,i-n1):], threshold, distance, delta) for i in xrange(delta, n1+n2-delta)]
+    return max(lcss)
+
+def normalizedAlignedLCSS(l1, l2, threshold, distance, delta, lengthMethod = min):
+    return float(alignedLCSS(l1, l2, threshold, distance, delta))/lengthMethod(len(l1), len(l2))
+
+def alignedDLCSS(l1, l2, threshold, distance, delta, lengthMethod = min):
+    return 1-normalizedAlignedLCSS(l1, l2, threshold, distance, delta, lengthMethod)
 
 def framesToTime(nFrames, frameRate, initialTime = (0.,0.,0.)):
     'returns hour, minutes and seconds'