# HG changeset patch
# User Nicolas Saunier <nicolas.saunier@polymtl.ca>
# Date 1356132816 18000
# Node ID f2cf16ad798f8364eadffc0407c9552db43854c5
# Parent  dbe7e53334d712b24bc7d3557e920793ebcb6dc9
added LCSS for trajectories

diff -r dbe7e53334d7 -r f2cf16ad798f python/moving.py
--- a/python/moving.py	Fri Dec 21 18:20:12 2012 -0500
+++ b/python/moving.py	Fri Dec 21 18:33:36 2012 -0500
@@ -171,6 +171,12 @@
         '2-norm distance (Euclidean distance)'
         return sqrt(self.norm2Squared())
 
+    def norm1(self):
+        return abs(self.x)+abs(self.y)
+    
+    def normMax(self):
+        return max(abs(self.x),abs(self.y))
+
     def aslist(self):
         return [self.x, self.y]
 
@@ -368,6 +374,9 @@
     def length(self):
         return len(self.positions[0])
 
+    def __len__(self):
+        return self.length()
+
     def addPositionXY(self, x, y):
         self.positions[0].append(x)
         self.positions[1].append(y)
@@ -496,6 +505,14 @@
 
     # version 2: use shapely polygon contains
 
+    @staticmethod
+    def norm2LCSS(t1, t2, threshold):
+        return utils.LCSS(t1, t2, threshold, Point.distanceNorm2)
+
+    @staticmethod
+    def normMaxLCSS(t1, t2, threshold):
+        return utils.LCSS(t1, t2, threshold, lambda p1, p2: (p1-p2).normMax())
+
 ##################
 # Moving Objects
 ##################
diff -r dbe7e53334d7 -r f2cf16ad798f python/tests/moving.txt
--- a/python/tests/moving.txt	Fri Dec 21 18:20:12 2012 -0500
+++ b/python/tests/moving.txt	Fri Dec 21 18:33:36 2012 -0500
@@ -74,3 +74,8 @@
 (0.500000,0.500000)
 >>> t1.getTrajectoryInPolygon(np.array([[10,10],[14,10],[14,13],[10,13]])).length()
 0
+
+>>> Trajectory.norm2LCSS(t1, t1, 0.1)
+3
+>>> Trajectory.normMaxLCSS(t1, t1, 0.1)
+3
diff -r dbe7e53334d7 -r f2cf16ad798f python/utils.py
--- a/python/utils.py	Fri Dec 21 18:20:12 2012 -0500
+++ b/python/utils.py	Fri Dec 21 18:33:36 2012 -0500
@@ -166,12 +166,12 @@
 #########################
 
 def LCSS(l1, l2, threshold, distance):
-    """returns the longest common subsequence similarity
-    based on the threshold on distance between two elements of lists l1, l2"""
-    from numpy import zeros
+    '''returns the longest common subsequence similarity
+    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))
+    similarity = zeros((m+1,n+1), dtype = npint)
     for i in xrange(1,m+1):
         for j in xrange(1,n+1):
             if distance(l1[i-1], l2[j-1])<threshold: