scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
This method minimizes the Lagrangian dual L of the entropy, which is defined for conditional models as
L(theta) = sum_w q(w) log Z(w; theta)
• sum_{w,x} q(w,x) [theta . f(w,x)]
Note that both sums are only over the training set {w,x}, not the entire sample space, since q(w,x) = 0 for all w,x
not in the training set.
The partial derivatives of L are:
dL / dtheta_i = K_i - E f_i(X, Y)
where the expectation is as defined above.
Methods
beginlogging(filename[, Enable logging params for each fn evaluation to files named ‘filename.freq.pickle’,
freq])
‘filename.(2*freq).pickle’, ...
clearcache() Clears the interim results of computations depending on the
crossentropy(fx[, Returns the cross entropy H(q, p) of the empirical
log_prior_x, base])
dual([params,
The entropy dual function is defined for conditional models as
ignorepenalty])
endlogging() Stop logging param values whenever setparams() is called.
entropydual([params, Computes the Lagrangian dual L(theta) of the entropy of the
ignorepenalty, ignoretest])
expectations() The vector of expectations of the features with respect to the
fit([algorithm]) Fits the conditional maximum entropy model subject to the
grad([params,
Computes or estimates the gradient of the entropy dual.
ignorepenalty])
log(params) This method is called every iteration during the optimization process.
lognormconst() Compute the elementwise log of the normalization constant
logparams() Saves the model parameters if logging has been
logpmf() Returns a (sparse) row vector of logarithms of the conditional probability mass
function (pmf) values p(x | c) for all pairs (c, x), where c are contexts and x are
points in the sample space.
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, Sets callback functions to be called every iteration, every function evaluation, or
callback_dual, ...]) every gradient evaluation.
setfeaturesandsamplespace(f, Creates a new matrix self.F of features f of all points in the
samplespace)
setparams(params) Set the parameter vector to params, replacing the existing parameters.
setsmooth(sigma) Speficies that the entropy dual and gradient should be computed with a quadratic
penalty term on magnitude of the parameters.
3.8. Maximum entropy models (scipy.maxentropy) 257