docs: def train(self, data, **kwargs):
          """
          Calculate the standard deviations and means in the training data
          """
          self.data = data
          for i in xrange(0,data.shape[1]):
              column_mean = np.mean(data.icol(i))
              column_stdev = np.std(data.icol(i))
  
              #Have to do += or "list" type will fail (ie with append)
              self.column_means += [column_mean]
              self.column_stdevs += [column_stdev]
  
          self.data = self.predict(data)