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I think this is probably related to #5.
When I try to use the ConditionalModel
class, I get TypeError: __init__() missing 2 required positional arguments: 'features' and 'samplespace'
from the call super(ConditionalModel, self).__init__()
in ConditonalModel.__init__()
.
Is this due to a current round of refactoring? Should it be necessary to pass feature functions and sample space in to ConditionalModel
? It looks like it wasn't always necessary (e.g. this old python 2 example https://github.com/stefanv/scipy3/blob/master/scipy/maxentropy/examples/conditionalexample2.py).
If I comment out the call to super
, then the fit
function fails because model
is not defined. Not sure if these things are directly related, or if this is a separate issue.
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