with open(self.indexPath) as f:

		reader = f.read(self)

		# loop over the rows in the index
		for row in reader:
			# parse out the image ID and features, then compute the
			# chi-squared distance between the features in our index
			# and our query features
			features = [float(x) for x in row[1:]]
			d = self.chi2_distance(features, queryFeatures)

			# now that we have the distance between the two feature
			# vectors, we can udpate the results dictionary -- the
			# key is the current image ID in the index and the
			# value is the distance we just computed, representing
			# how 'similar' the image in the index is to our query
			results[row[0]] = d

		# close the reader
	f.close()