text: import anfis
  import membership.mfDerivs
  import membership.membershipfunction
  import numpy
  
  ts = numpy.loadtxt("trainingSet.txt", usecols=[1,2,3])#numpy.loadtxt('c:\\Python_fiddling\\myProject\\MF\\trainingSet.txt',usecols=[1,2,3])
  X = ts[:,0:2]
  Y = ts[:,2]
  
  mf = [[['gaussmf',{'mean':0.,'sigma':1.}],['gaussmf',{'mean':-1.,'sigma':2.}],['gaussmf',{'mean':-4.,'sigma':10.}],['gaussmf',{'mean':-7.,'sigma':7.}]],
              [['gaussmf',{'mean':1.,'sigma':2.}],['gaussmf',{'mean':2.,'sigma':3.}],['gaussmf',{'mean':-2.,'sigma':10.}],['gaussmf',{'mean':-10.5,'sigma':5.}]]]
  
  
  mfc = membership.membershipfunction.MemFuncs(mf)
  anf = anfis.ANFIS(X, Y, mfc)
  anf.trainHybridJangOffLine(epochs=20)
  print(round(anf.consequents[-1][0],6))
  print(round(anf.consequents[-2][0],6))
  print(round(anf.fittedValues[9][0],6))
  if round(anf.consequents[-1][0],6) == -5.275538 and round(anf.consequents[-2][0],6) == -1.990703 and round(anf.fittedValues[9][0],6) == 0.002249:
  	print('test is good')
  
  print("Plotting errors")
  anf.plotErrors()
  print("Plotting results")
  anf.plotResults()
  
  X = numpy.array([[10,10]])
  print(anfis.predict(anf,X))