repo/traffic-intelligence: python/utils.py comparison

work on indicator similarity

author	Nicolas Saunier <nicolas.saunier@polymtl.ca>
date	Wed, 17 Jul 2013 18:19:08 -0400
parents	2ea8584aa80a
children	387cc0142211

comparison

equal deleted inserted replaced

-:2ea8584aa80a
+:2e6b8610bcaa
 def computeAlignment(self):
 from numpy import mean
 return mean([j-i for i,j in self.subSequenceIndices])
-def _computeNormalized(self, l1, l2):
+def _computeNormalized(self, l1, l2, computeSubSequence = False):
 ''' compute the normalized LCSS
 ie, the LCSS divided by the min or mean of the indicator lengths (using lengthFunc)
 lengthFunc = lambda x,y:float(x,y)/2'''
-return float(self._compute(l1, l2))/self.lengthFunc(len(l1), len(l2))
+return float(self._compute(l1, l2, computeSubSequence))/self.lengthFunc(len(l1), len(l2))
-def computeNormalized(self, l1, l2):
+def computeNormalized(self, l1, l2, computeSubSequence = False):
-return self._computeNormalized(l1, l2)
+return self._computeNormalized(l1, l2, computeSubSequence)
-def _computeDistance(self, l1, l2):
+def _computeDistance(self, l1, l2, computeSubSequence = False):
 ''' compute the LCSS distance'''
-return 1-self._computeNormalized(l1, l2)
+return 1-self._computeNormalized(l1, l2, computeSubSequence)
-def computeDistance(self, l1, l2):
+def computeDistance(self, l1, l2, computeSubSequence = False):
-return self._computeDistance(l1, l2)
+return self._computeDistance(l1, l2, computeSubSequence)
 #########################
 # plotting section
 #########################

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