scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
model.beginlogging(filename[, Enable logging params for each fn evaluation to files named
freq])
‘filename.freq.pickle’, ‘filename.(2*freq).pickle’, ...
model.endlogging() Stop logging param values whenever setparams() is called.
model.clearcache() Clears the interim results of computations depending on the
model.crossentropy(fx[,
log_prior_x, base])
Returns the cross entropy H(q, p) of the empirical
model.dual([params,
ignorepenalty, ignoretest])
Computes the Lagrangian dual L(theta) of the entropy of the
model.fit(K[, algorithm]) Fit the maxent model p whose feature expectations are given
model.grad([params,
ignorepenalty])
Computes or estimates the gradient of the entropy dual.
model.log(params) This method is called every iteration during the optimization process.
model.logparams() Saves the model parameters if logging has been
model.normconst() Returns the normalization constant, or partition function, for the current
model.
model.reset([numfeatures]) Resets the parameters self.params to zero, clearing the cache variables
dependent on them.
model.setcallback([callback, Sets callback functions to be called every iteration, every function
callback_dual, ...])
evaluation, or every gradient evaluation.
model.setparams(params) Set the parameter vector to params, replacing the existing parameters.
model.setsmooth(sigma) Speficies that the entropy dual and gradient should be computed with a
quadratic penalty term on magnitude of the parameters.
model.expectations() The vector E_p[f(X)] under the model p_params of the vector of
model.lognormconst() Compute the log of the normalization constant (partition
model.logpmf() Returns an array indexed by integers representing the
model.pmf_function([f]) Returns the pmf p_theta(x) as a function taking values on the model’s
sample space.
model.setfeaturesandsamplespace(f, Creates a new matrix self.F of features f of all points in the
samplespace)
beginlogging(filename, freq=10)
Enable logging params for each fn evaluation to files named ‘filename.freq.pickle’, ‘filename.(2*freq).pickle’,
... each ‘freq’ iterations.
endlogging()
Stop logging param values whenever setparams() is called.
clearcache()
Clears the interim results of computations depending on the parameters and the sample.
crossentropy(fx, log_prior_x=None, base=2.7182818284590451)
Returns the cross entropy H(q, p) of the empirical distribution q of the data (with the given feature matrix fx)
with respect to the model p. For discrete distributions this is defined as:
H(q, p) = - n^{-1} sum_{j=1}^n log p(x_j)
where x_j are the data elements assumed drawn from q whose features are given by the matrix fx = {f(x_j)},
j=1,...,n.
The ‘base’ argument specifies the base of the logarithm, which defaults to e.
For continuous distributions this makes no sense!
dual(params=None, ignorepenalty=False, ignoretest=False)
Computes the Lagrangian dual L(theta) of the entropy of the model, for the given vector theta=params. Minimizing
this function (without constraints) should fit the maximum entropy model subject to the given
constraints. These constraints are specified as the desired (target) values self.K for the expectations of the
feature statistic.
3.8. Maximum entropy models (scipy.maxentropy) 249