file2_contents = file2.read().split('\n')
		f2contents = file2_contents[1].split(',') # index 0 contains feature headers

		data1 = []
		data2 = []
		for k in range(0, len(f1contents)):
			try:
				f1contents[k] = float(f1contents[k])
				data1.append(f1contents[k])
			except:
				pass
		for k in range(0, len(f2contents)):
			try:
				f2contents[k] = float(f2contents[k])
				data2.append(f2contents[k])
			except:
				pass

		indices = Distance.prune(data1, data2)
		#print("Euclidean: " + str(Distance.euclidean_distance(data1, data2, indices)))
		output.write(files[i] + "," + files[j] + "," + "euclidean" + "," + str(Distance.euclidean_distance(data1, data2, indices)) + "\n")
		#print("City: " + str(Distance.city_distance(data1, data2)))
		output.write(files[i] + "," + files[j] + "," + "city" + "," + str(Distance.city_distance(data1, data2)) + "\n")
		#print("Chebychev: " + str(Distance.chebychev_distance(data1, data2)))
		output.write(files[i] + "," + files[j] + "," + "chebychev" + "," + str(Distance.chebychev_distance(data1, data2)) + "\n")
		#print("Cosine: " + str(Distance.cosine_difference(data1, data2)))
		output.write(files[i] + "," + files[j] + "," + "cosine" + "," + str(Distance.cosine_difference(data1, data2)) + "\n")
		#print("Correlation: " + str(Distance.correlation_distance(data1, data2)))
		output.write(files[i] + "," + files[j] + "," + "correlation" + "," + str(Distance.correlation_distance(data1, data2)) + "\n")