normalizer/new-main.py at master · tinchoa/normalizer · GitHub

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from histograma import Histograma
from original import Original
#from normalizerOffline import normalizer
from maxMin_Normalizer import maxMin_Normalizer

'''
added by Martin
'''
import time,sys
import numpy as np
from sklearn.metrics import mean_squared_error


####ate aqui

class NewMain:
	def run(self,data,flag): #flag =1 streamning, flag=0 batch
		beg=time.time()

		#data = open("classes-17.out", "r")
		saida = open("classes-17-norm-histo.out", "w")

		self.hists = []
		hists = self.hists
		resFinal=[]
		features = []

		if flag==1:
			#tamanhoJanela = int(sys.argv[1]) #as paper
			tamanhoJanela=500
			linha = data.readline()

			while linha !="":
				janela = []
				while linha !="" and len(janela) < tamanhoJanela:
					tmp1 = linha.strip("\n").split(",")[5:-1]#removing IPsrc,IPdst,portsrc,portdsc,proto,class
					tmp2 = []
					for i in tmp1:
						tmp2.append(float(i))
					janela.append(tmp2)
					linha = data.readline()
				#processa janela
				#print len(janela)


				for i in range(len(janela)):
					for j in range(len(janela[i])):
						if (len(features)-1) < j:
							features+=[[]]
						features[j].append(janela[i][j])

				for j in range(len(features)):
					if len(hists) < j+1:
						hists.append(Histograma(features[j]))
					else:
						hists[j].updateHistograma(features[j])
					#    print j, "hist", hists[j].hist, hists[j].pivo
						#print features[j]


				resultados = []

				for i in range(len(janela)):
					resultados.append([])
					for j in range(len(janela[i])):
						resultados[-1].append(hists[j].getNormalizedValues(janela[i][j])[0])

		 		for k in resultados:
		 			tmp = []
		 			tmp2= []
		 	 		for l in k:
		 	 			tmp.append(str(l))
		 	 			tmp2.append(l)
		 	 		resFinal.append(tmp2)
		 	 		#linhaSaida =  ",".join(tmp)
		 	 		#saida.write(linhaSaida+"\n")

	 	if flag==0:
			tamanhoJanela=len(data)
			for i in range(tamanhoJanela):
				for j in range(len(data[i])):
					if (len(features)-1) < j:
						features+=[[]]
					features[j].append(data[i][j])

			for j in range(len(features)):
				if len(hists) < j+1:
					hists.append(Histograma(features[j]))
				else:
					hists[j].updateHistograma(features[j])
				#    print j, "hist", hists[j].hist, hists[j].pivo
					#print features[j]


			resultados = []

			for i in range(len(data)):
				resultados.append([])
				for j in range(len(data[i])):
					resultados[-1].append(hists[j].getNormalizedValues(data[i][j])[0])

	 		for k in resultados:
	 			tmp = []
	 			tmp2= []
	 	 		for l in k:
	 	 			tmp.append(str(l))
	 	 			tmp2.append(l)
	 	 		resFinal.append(tmp2)
			end=time.time()-beg

	# 	# saida.write(str('processing time : '+str(end))+'\n')


		##return hists,end #to retunr the object
		return resFinal
	# 	#print janela


if __name__ == "__main__":

	output_file=open(str(sys.argv[1])+'-output','w')
	print 'proposal starting... '+'\n'
	proposal,timeProposal=Main().run()
	print 'proposal finished... '+'\n'

	print 'original started...'+'\n'
	old=Original().run()

	print 'maxMin started....'+'\n'
	maxMin,timeMaxmin=maxMin_Normalizer().run()

	'''
	to calculate the mean square error
	'''
	original_proposal=[]
	original_max=[]
	original=np.asfarray(old)
	proposal=np.asfarray(proposal)
	maxMin=np.asfarray(maxMin)
	for i in range(len(proposal[0])):
	  	original_proposal.append(mean_squared_error(original[:,i],proposal[:,i]))
	  	original_max.append(mean_squared_error(original[:,i],maxMin[:,i]))

	# #MSEproposal=mean_squared_error(original,proposal)
	# #MSEmaxMin=mean_squared_error(original,maxMin)

	MSEproposal=sum(original_proposal)/float(len(original_proposal))
	MSEmaxMin=sum(original_max)/float(len(original_max))

	output_file.write(str(MSEproposal)+','+str(timeProposal)+'\n')
	# #output_file.write('Proposal Procesing time: '+ str(timeProposal)+'\n')
	output_file.write(str(MSEmaxMin)+','+str(timeMaxmin)+'\n')
	# #output_file.write('MaxMin Procesing time: '+ str(timeMaxmin)+'\n')


	output_file.close()