scipy tutorial - Baustatik-Info-Server

SciPy Reference Guide, Release 0.8.dev
conditionalmodel.dual([params, The entropy dual function is defined for conditional models as
ignorepenalty])
conditionalmodel.expectations()
The vector of expectations of the features with respect to the
conditionalmodel.fit([algorithm]) Fits the conditional maximum entropy model subject to the
conditionalmodel.lognormconst()
Compute the elementwise log of the normalization constant
conditionalmodel.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.
dual(params=None, ignorepenalty=False)
The entropy dual function is defined for conditional models as
L(theta) = sum_w q(w) log Z(w; theta)
or equivalently as
• sum_{w,x} q(w,x) [theta . f(w,x)]
L(theta) = sum_w q(w) log Z(w; theta) - (theta . k)
where K_i = sum_{w, x} q(w, x) f_i(w, x), and where q(w) is the empirical probability mass function derived
from observations of the context w in a training set. Normally q(w, x) will be 1, unless the same class label is
assigned to the same context more than once.
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 entropy dual function is proportional to the negative log likelihood.
Compare to the entropy dual of an unconditional model:
L(theta) = log(Z) - theta^T . K
expectations()
The vector of expectations of the features with respect to the distribution p_tilde(w) p(x | w), where p_tilde(w)
is the empirical probability mass function value stored as self.p_tilde_context[w].
fit(algorithm=’CG’)
Fits the conditional maximum entropy model subject to the constraints
sum_{w, x} p_tilde(w) p(x | w) f_i(w, x) = k_i
for i=1,...,m, where k_i is the empirical expectation
k_i = sum_{w, x} p_tilde(w, x) f_i(w, x).
lognormconst()
Compute the elementwise log of the normalization constant (partition function) Z(w)=sum_{y in Y(w)}
exp(theta . f(w, y)). The sample space must be discrete and finite. This is a vector with one element for each
context w.
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. The order of these is log p(x | c) =
logpmf()[c * numsamplepoints + x].
258 Chapter 3. Reference