scipy.maxentropy.basemodel

class scipy.maxentropy.basemodel

A base class providing generic functionality for both small and large maximum entropy models. Cannot be instantiated.

Methods

beginlogging(filename[, freq])	Enable logging params for each fn evaluation to files named ‘filename.freq.pickle’, ‘filename.(2*freq).pickle’, ...
clearcache()	Clears the interim results of computations depending on the
crossentropy(fx[, log_prior_x, base])	Returns the cross entropy H(q, p) of the empirical
dual([params, ignorepenalty, ignoretest])	Computes the Lagrangian dual L(theta) of the entropy of the
endlogging()	Stop logging param values whenever setparams() is called.
entropydual([params, ignorepenalty, ignoretest])	Computes the Lagrangian dual L(theta) of the entropy of the
fit(K[, algorithm])	Fit the maxent model p whose feature expectations are given
grad([params, ignorepenalty])	Computes or estimates the gradient of the entropy dual.
log(params)	This method is called every iteration during the optimization process.
logparams()	Saves the model parameters if logging has been
normconst()	Returns the normalization constant, or partition function, for the current model.
reset([numfeatures])	Resets the parameters self.params to zero, clearing the cache variables dependent on them.
setcallback([callback, callback_dual, ...])	Sets callback functions to be called every iteration, every function evaluation, or every gradient evaluation.
setparams(params)	Set the parameter vector to params, replacing the existing parameters.
setsmooth(sigma)	Specifies that the entropy dual and gradient should be computed with a quadratic penalty term on magnitude of the parameters.