def retrieveEstimationError(self,x,target):

		#setting number of inputs and number of outputs in the neural network
		_ , xColumns = x.shape
		_ , targetColumns = target.shape
		self.neuralNetwork.n_in = xColumns
		self.neuralNetwork.n_out = targetColumns

		self.neuralNetwork.initialize_weights()

		self.neuralNetwork.backpropagation(x,target,maxIterations=self.maxIterations)

		# Network result after training
		estimation = self.neuralNetwork.feed_forward(x)

		estimationError = EstimationError(estimatedValues=estimation,targetValues=target)
		estimationError.computeErrors()
		totalError = estimationError.getTotalError()
		return totalError
	target = numpy.array([[0]
			  ,[1]
			  ,[1]
			  ,[0]])

	#setting number of inputs and number of outputs in the neural network
	_ , xColumns = x.shape
	_ , targetColumns = target.shape
	neuralNetwork = NeuralNetwork(learning_rate=0.1,n_in=xColumns,n_hidden=2,n_out=targetColumns,activation='tanh',momentum=0.9)

	neuralNetwork.initialize_weights()
	neuralNetwork.backpropagation(x,target,maxIterations=10000, batch=False)

	# Network result after training
	estimation = neuralNetwork .feed_forward(x)

	printSeparator = "----------------"
	
	print "Estimated values:"
	print estimation
	print printSeparator
	print "Target values:"
	print target
	print printSeparator

	estimationError = EstimationError(estimatedValues=estimation,targetValues=target)
	estimationError.computeErrors()
	totalError = estimationError.getTotalError()
	print "Total Error: %s" %(totalError)