Hand down options as list to libsvm (pattern.vector)
@@ -3338,7 +3338,7 @@ class SVM(Classifier):
)
# Cache the model and the feature hash.
# SVM.train() will remove the cached model (since it needs to be retrained).
- self._model = (f(y, x, o.encode("utf-8")), H1, H2, H3)
+ self._model = (f(y, x, o.split()), H1, H2, H3)
def _classify(self, document, probability=False):
""" Calls libsvm.svm_predict() with the cached model.
