![]() Returns the estimated density p_theta(x) at the point x with feature statistic fx = f(x). Returns the log of the estimated density p(x) = p_theta(x) at the point x. This function approximates both the feature expectation vector The tails should be fatter than the model. Space in more than about 4 dimensions or a large discrete space likeĪll possible sentences in a natural language.Īpproximating the expectations by sampling requires an instrumentalĭistribution that should be close to the model for fast convergence. Large to sum or integrate over in practice, like a continuous sample Approximation is necessary when the sample space is too Integrating over a sample space) but approximately (by Monte CarloĮstimation). ![]() The model expectations are not computed exactly (by summing or tfeaturesandsamplespace(f, samplespace)Ĭreates a new matrix self.F of features f of all points in theĪ maximum-entropy (exponential-form) model on a large sample Returns the pmf p_theta(x) as a function taking values on the model’s sample space. Returns an array indexed by integers representing the The vector E_p under the model p_params of the vector ofĬompute the log of the normalization constant (partition model ( f=None, samplespace=None ) ¶Ī maximum-entropy (exponential-form) model on a discrete sample Speficies that the entropy dual and gradient should be computed with a quadratic penalty term on magnitude of the parameters.Ĭlass scipy.maxentropy. Set the parameter vector to params, replacing the existing parameters. Sets callback functions to be called every iteration, every function evaluation, or every gradient evaluation. Resets the parameters self.params to zero, clearing the cache variables dependent on them. ![]() Returns the normalization constant, or partition function, for the current model. Saves the model parameters if logging has been This method is called every iteration during the optimization process. Returns the cross entropy H(q, p) of the empiricalīasemodel.dual()Ĭomputes the Lagrangian dual L(theta) of the entropy of theįit the maxent model p whose feature expectations are givenĬomputes or estimates the gradient of the entropy dual. Stop logging param values whenever setparams() is called.Ĭlears the interim results of computations depending on theīasemodel.crossentropy(fx) Cannot be instantiated.Įnable logging params for each fn evaluation to files named ‘’, ‘filename.(2*freq).pickle’. basemodel ¶Ī base class providing generic functionality for both small and ![]()
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