@Override
public double getUnnormalizedLogProbability(Assignment assignment) {
Preconditions.checkArgument(assignment.containsAll(getVars().getVariableNumsArray()));
Tensor inputFeatureVector =
(Tensor) assignment.getValue(getInputVariable().getOnlyVariableNum());
if (conditionalVars.size() == 0) {
// No normalization for any conditioned-on variables. This case
// allows a more efficient implementation than the default
// in ClassifierFactor.
VariableNumMap outputVars = getOutputVariables();
Tensor outputTensor =
SparseTensor.singleElement(
outputVars.getVariableNumsArray(),
outputVars.getVariableSizes(),
outputVars.assignmentToIntArray(assignment),
1.0);
Tensor featureIndicator = outputTensor.outerProduct(inputFeatureVector);
return logWeights.innerProduct(featureIndicator).getByDimKey();
} else {
// Default to looking up the answer in the output log probabilities
int[] outputIndexes = getOutputVariables().assignmentToIntArray(assignment);
Tensor logProbs = getOutputLogProbTensor(inputFeatureVector);
return logProbs.getByDimKey(outputIndexes);
}
}
@Override
protected Tensor getOutputLogProbTensor(Tensor inputFeatureVector) {
Tensor logProbs = logWeights.innerProduct(inputFeatureVector.relabelDimensions(inputVarNums));
if (conditionalVars.size() > 0) {
Tensor probs = logProbs.elementwiseExp();
Tensor normalizingConstants = probs.sumOutDimensions(conditionalVars.getVariableNumsArray());
logProbs =
probs.elementwiseProduct(normalizingConstants.elementwiseInverse()).elementwiseLog();
}
return logProbs;
}