parameters: (self, fileName, data)
docstring: load a trained classifier from a file.  Also provide the data on which
          the classifier was trained.  It assumes the underlying binary classifier is
          an SVM
docstring_summary: load a trained classifier from a file.  Also provide the data on which
          the classifier was trained.  It assumes the underlying binary classifier is
          an SVM
docstring_tokens: load a trained classifier from a file . Also provide the data on which the classifier was trained . It assumes the underlying binary classifier is an SVM
function: def load(self, fileName, data) :
          """load a trained classifier from a file.  Also provide the data on which
          the classifier was trained.  It assumes the underlying binary classifier is
          an SVM"""
  
          from PyML import svm
          Classifier.train(self, data)
          file_handle = open(fileName)
          numClasses = self.labels.numClasses
          self.classifiers = [self.classifier.__class__(self.classifier)
                              for i in range(numClasses)]
          for i in range(numClasses) :
              datai = data.__class__(data, deepcopy = self.classifier.deepcopy)
              datai =  oneAgainstRest(datai, data.labels.classLabels[i])
              self.classifiers[i] = svm.loadSVM(file_handle, datai)
function_tokens: def load ( self , fileName , data ) : from PyML import svm Classifier . train ( self , data ) file_handle = open ( fileName ) numClasses = self . labels . numClasses self . classifiers = [ self . classifier . __class__ ( self . classifier ) for i in range ( numClasses ) ] for i in range ( numClasses ) : datai = data . __class__ ( data , deepcopy = self . classifier . deepcopy ) datai = oneAgainstRest ( datai , data . labels . classLabels [ i ] ) self . classifiers [ i ] = svm . loadSVM ( file_handle , datai )