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public interface ParameterizedFactor
A factor that supports taking derivatives with respect to its continuous variables. For example, a Gaussian factor can support derivatives with respect to its mean and precision. $Id: ParameterizedFactor.java,v 1.1 2007/10/22 21:37:44 mccallum Exp $
Method Summary | |
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double |
sumGradLog(Factor q,
Variable param,
Assignment assn)
Computes the expected derivative of the log factor value. |
Methods inherited from interface cc.mallet.grmm.types.Factor |
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almostEquals, almostEquals, argmax, assignmentIterator, asTable, containsVar, divideBy, dumpToString, duplicate, entropy, exponentiate, extractMax, extractMax, extractMax, getVariable, isNaN, logValue, logValue, logValue, marginalize, marginalize, marginalize, marginalizeOut, marginalizeOut, multiply, multiplyBy, normalize, prettyOutputString, sample, slice, sum, value, value, varSet |
Method Detail |
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double sumGradLog(Factor q, Variable param, Assignment assn)
sum_{y} q(y) dlog f(y) / d theta, where y are the outcomes of the discrete varables in the factor, f(y) is the factor value, and theta is the vector of continuous variables in the factor. q is a user-specified distribution to take the expectation with respect to.The factor q specifies with variables to sum over. The summation will be over all the variables in q.varSet(), and the rest of the variables will be used
q
- Distribution to take with respect to (need not be normalized).
q.varSet() should be all of the variables of this factor, except for one continuous variableparam
- Parameter to take gradient with respect to.
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