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.
I think this is probably related to #5.
When I try to use the
ConditionalModelclass, I getTypeError: __init__() missing 2 required positional arguments: 'features' and 'samplespace'from the callsuper(ConditionalModel, self).__init__()inConditonalModel.__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 thefitfunction fails becausemodelis not defined. Not sure if these things are directly related, or if this is a separate issue.