def preProcessExamples(self, allExamples):
# Duplicates cannot be removed here, as they should only be removed from the training set. This is done
# in the classifier.
# if "no_duplicates" in self.styles:
# count = len(allExamples)
# print >> sys.stderr, " Removing duplicates,",
# allExamples = ExampleUtils.removeDuplicates(allExamples)
# print >> sys.stderr, "removed", count - len(allExamples)
if "normalize" in self.styles:
print >> sys.stderr, " Normalizing feature vectors"
ExampleUtils.normalizeFeatureVectors(allExamples)
return allExamples
def preProcessExamples(self, allExamples):
# Duplicates cannot be removed here, as they should only be removed from the training set. This is done
# in the classifier.
# if "no_duplicates" in self.styles:
# count = len(allExamples)
# print >> sys.stderr, " Removing duplicates,",
# allExamples = ExampleUtils.removeDuplicates(allExamples)
# print >> sys.stderr, "removed", count - len(allExamples)
if "normalize" in self.styles:
print >> sys.stderr, " Normalizing feature vectors"
ExampleUtils.normalizeFeatureVectors(allExamples)
return allExamples
def preProcessExamples(self, allExamples):
if "normalize" in self.styles:
print >> sys.stderr, " Normalizing feature vectors"
ExampleUtils.normalizeFeatureVectors(allExamples)
return allExamples
def preProcessExamples(self, allExamples):
if "normalize" in self.styles:
print >> sys.stderr, " Normalizing feature vectors"
ExampleUtils.normalizeFeatureVectors(allExamples)
return allExamples
