Use Levenshtein module when available for performance reasons

(it's 300 times faster).

NanScan/Levenshtein.py

@@ -1,65 +0,0 @@
-#   Copyright (C) 2008 by Albert Cervera i Areny
-#   albert@nan-tic.com
-#
-#   This program is free software; you can redistribute it and/or modify 
-#   it under the terms of the GNU General Public License as published by
-#   the Free Software Foundation; either version 2 of the License, or 
-#   (at your option) any later version. 
-#
-#   This program is distributed in the hope that it will be useful, 
-#   but WITHOUT ANY WARRANTY; without even the implied warranty of 
-#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the 
-#   GNU General Public License for more details.
-#
-#   You should have received a copy of the GNU General Public License 
-#   along with this program; if not, write to the
-#   Free Software Foundation, Inc.,
-#   59 Temple Place - Suite 330, Boston, MA  02111-1307, USA. 
-
-
-# TODO: If available, wrap levenshtein C implementation
-
-class Levenshtein:
-
-	@staticmethod
-	def levenshtein( text1, text2 ):
-		# Levenshtein distance if one string is empty, is the
-		# length of the other string, len(text) inserts.
-		if len(text1) == 0:
-			return len(text2)
-		if len(text2) == 0:
-			return len(text1)
-
-		# Build array of len(text1) * len(text2)
-		d = [ [0] * len(text2) ] * len(text1)
-
-		for i in range(len(text1)):
-			d[i][0] = i
-
-		for j in range(len(text2)):
-			d[0][j] = j
-
-		for i in range(len(text1)-1): 
-			for j in range(len(text2)-1):
-				ip = i+1
-				jp = j+1
-				if text1[ip] == text2[jp]:
-					cost = 0
-				else:
-					cost = 1
-
-				d[ip][jp] = min( 
-					d[ip-1][jp] + 1,      # deletion
-					d[ip][jp-1] + 1,      # insertion
-					d[ip-1][jp-1] + cost  # substitution
-				)
-		return d[len(text1)-1][len(text2)-1]
-
-if __name__ == '__main__':
-	print Levenshtein.levenshtein( 'abc', 'abc' )
-	print Levenshtein.levenshtein( 'abcabc', 'abc' )
-	print Levenshtein.levenshtein( 'abcdef', 'abc' )
-	print Levenshtein.levenshtein( 'abcdef', 'bcd' )
-	print Levenshtein.levenshtein( 'bcdef', 'abc' )
-	for x in range(10000):
-		Levenshtein.levenshtein( 'text de la plantilla', 'text llarg que pot ser del document que tractem actualment' )

NanScan/LevenshteinDistance.py

@@ -0,0 +1,82 @@
+#   Copyright (C) 2008 by Albert Cervera i Areny
+#   albert@nan-tic.com
+#
+#   This program is free software; you can redistribute it and/or modify 
+#   it under the terms of the GNU General Public License as published by
+#   the Free Software Foundation; either version 2 of the License, or 
+#   (at your option) any later version. 
+#
+#   This program is distributed in the hope that it will be useful, 
+#   but WITHOUT ANY WARRANTY; without even the implied warranty of 
+#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the 
+#   GNU General Public License for more details.
+#
+#   You should have received a copy of the GNU General Public License 
+#   along with this program; if not, write to the
+#   Free Software Foundation, Inc.,
+#   59 Temple Place - Suite 330, Boston, MA  02111-1307, USA. 
+
+
+# Note that the file is called LevenshteinDistance.py so it doesn't collide
+# with Levenshtein module (when installed).
+
+# Try to use Levenshtein module (implemented in C). If not found fall back to
+# our own 300 times slower python implementation.
+try:
+	import Levenshtein as cLevenshtein
+	class Levenshtein:
+
+		@staticmethod
+		def levenshtein( text1, text2 ):
+			return cLevenshtein.distance( text1, text2 )
+except:
+	print "Warning: Levenshtein module not found. Using 300 times slower python implementation."
+
+	class Levenshtein:
+		@staticmethod
+		def levenshtein( text1, text2 ):
+			# Levenshtein distance if one string is empty, is the
+			# length of the other string, len(text) inserts.
+			if len(text1) == 0:
+				return len(text2)
+			if len(text2) == 0:
+				return len(text1)
+
+			# Build array of len(text1) * len(text2)
+			len1 = len(text1) + 1
+			len2 = len(text2) + 1
+
+			d = []
+			for x in range(len1):
+				d.append( [0] * len2 )
+
+			for i in range(len1):
+				d[i][0] = i
+
+			for j in range(len2):
+				d[0][j] = j
+
+			for i in range(1,len1): 
+				for j in range(1,len2):
+					ip = i-1
+					jp = j-1
+					if text1[ip] == text2[jp]:
+						cost = 0
+					else:
+						cost = 1
+
+					d[i][j] = min( 
+						d[i-1][j] + 1,      # deletion
+						d[i][j-1] + 1,      # insertion
+						d[i-1][j-1] + cost  # substitution
+					)
+			return d[len(text1)-1][len(text2)-1]
+
+if __name__ == '__main__':
+	print Levenshtein.levenshtein( 'abc', 'abc' )
+	print Levenshtein.levenshtein( 'abcabc', 'abc' )
+	print Levenshtein.levenshtein( 'abcdef', 'abc' )
+	print Levenshtein.levenshtein( 'abcdef', 'bcd' )
+	print Levenshtein.levenshtein( 'bcdef', 'abc' )
+	for x in range(10000):
+		Levenshtein.levenshtein( 'text de la plantilla', 'text llarg que pot ser del document que tractem actualment' )

NanScan/Recognizer.py

@@ -27,7 +27,7 @@ from Template import *
 from Document import *
 from Trigram import *
 from Hamming import *
-from Levenshtein import *
+from LevenshteinDistance import *
 from Translator import *
 
 import tempfile