scipy.maxentropy.model¶

class scipy.maxentropy.model(f=None, samplespace=None)

A maximum-entropy (exponential-form) model on a discrete sample space.

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 expectations() The vector E_p[f(X)] under the model p_params of the vector of 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. lognormconst() Compute the log of the normalization constant (partition logparams() Saves the model parameters if logging has been logpmf() Returns an array indexed by integers representing the normconst() Returns the normalization constant, or partition function, for the current model. pmf() Returns an array indexed by integers representing the values of the probability mass function (pmf) at each point in the sample space under the current model (with the current parameter vector self.params). pmf_function([f]) Returns the pmf p_theta(x) as a function taking values on the model’s sample space. probdist() Returns an array indexed by integers representing the values of the probability mass function (pmf) at each point in the sample space under the current model (with the current parameter vector self.params). 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. setfeaturesandsamplespace(f, samplespace) Creates a new matrix self.F of features f of all points in the 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.

Next topic

scipy.maxentropy.model.beginlogging