title: PyML: graphing the decision surface
body: <p>PyML has a function for graphing decision surfaces. </p>
  
  <p>First you need to tell PyML which data to use. Here I use a sparsevectordata with my feature vectors. This is the one I used to train my SVM.</p>
  
  <pre><code>demo2d.setData(training_vector)
  </code></pre>
  
  <p>Then you need to tell it which classifier you want to use. I give it a trained SVM.</p>
  
  <pre><code>demo2d.decisionSurface(best_svm, fileName = "dec.pdf")
  </code></pre>
  
  <p>However, I get this error message:</p>
  
  <pre><code>Traceback (most recent call last):
  **deleted by The Unfun Cat**
      demo2d.decisionSurface(best_svm, fileName = "dec.pdf")
    File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/PyML/demo/demo2d.py", line 140, in decisionSurface
      results = classifier.test(gridData)
    File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/PyML/evaluators/assess.py", line 45, in test
      classifier.verifyData(data)
    File "/opt/local/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/PyML/classifiers/baseClassifiers.py", line 55, in verifyData
      if len(misc.intersect(self.featureID, data.featureID)) != len(self.featureID) :
  AttributeError: 'SVM' object has no attribute 'featureID'
  </code></pre>