Joe Kington committed d837692

Now using,b) instead of to ensure compability with older versions of numpy

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         # Subtract the average paw (known a-priori from the training dataset)
         paw -= self.average_paw
         # Project the paw into eigenpaw-space
-        scores = 
+        scores =, self.basis_vecs) 
         # "whiten" the score so that all dimensions are equally important
         scores /= self.basis_stds
         # Select which template paw is the closest to the given paw...

     # Project the standardized paw data into the space specified by the
     # basis_vecs (i.e. project them into eigenpaw-space)
-    paw_scores = (paw_data - average_paw).dot(basis_vecs)
+    paw_scores = - average_paw, basis_vecs)
     # In order for the distance classification to be equally sensitive to
     # all dimensions of the score vector, we need to rescale things by the