title: pybrain what is total error and what does it tell us
body: <p>so I am very new to python and pybrain but I found a code online and ran my own data on it. When i look at the python shell all I see is </p>
  
  <pre><code>Total error: 0.119794950183
  Total error: 0.120078064472
  Total error: 0.119334171755
  Total error: 0.119215954708
  Total error: 0.119876371059
  Total error: 0.119621091587
  Total error: 0.119983293587
  Total error: 0.119849213731
  Total error: 0.119638354788
  Total error: 0.119574076444
  Total error: 0.119634911454
  Total error: 0.119601510912
  Total error: 0.119665039573
  Total error: 0.11944303853
  Total error: 0.11950617361
  Total error: 0.120088611572
  Total error: 0.119774446939
  Total error: 0.120016814426
  Total error: 0.119605678505
  Total error: 0.119998864263
  Total error: 0.120071472045
  Total error: 0.11973079242
  Total error: 0.119790825048
  Total error: 0.119558913137
  Total error: 0.12024443015
  Total error: 0.119525196587
  Total error: 0.12008456943
  Total error: 0.119641361568
  Total error: 0.119745707444
  Total error: 0.120065315199
  </code></pre>
  
  <p>1) what does total error mean and what is it doing </p>
  
  <p>here is the code </p>
  
  <pre><code>from pybrain.datasets import SupervisedDataSet
  from pybrain.tools.shortcuts import buildNetwork
  from pybrain.supervised.trainers import BackpropTrainer
  
  from pybrain.datasets            import ClassificationDataSet
  from pybrain.utilities           import percentError
  from pybrain.tools.shortcuts     import buildNetwork
  from pybrain.supervised.trainers import BackpropTrainer
  from pybrain.structure.modules   import SoftmaxLayer
  
  from pylab import ion, ioff, figure, draw, contourf, clf, show, hold, plot
  from scipy import diag, arange, meshgrid, where
  from numpy.random import multivariate_normal
  
  ds = SupervisedDataSet(2,1)
  
  tf = open('weather.csv','r')
  
  for line in tf.readlines():
      try:
          data = [float(x) for x in line.strip().split(',') if x != '']
          indata =  tuple(data[:2])
          outdata = tuple(data[2:])
          ds.addSample(indata,outdata)
      except ValueError,e:
              print "error",e,"on line"
  
  n = buildNetwork(ds.indim,8,8,ds.outdim,recurrent=True)
  t = BackpropTrainer(n,learningrate=0.01,momentum=0.5,verbose=True)
  t.trainOnDataset(ds,5000)
  t.testOnData(verbose=True)
  </code></pre>