/** Set a lower-bound estimate inside the rule returns full estimate. */
public final float estimateRuleCost(List<FeatureFunction> featureFunctions) {
if (null == featureFunctions) {
return 0;
} else {
float estcost = 0.0f;
for (FeatureFunction ff : featureFunctions) {
double mdcost = -ff.estimateLogP(this, -1) * ff.getWeight();
estcost += mdcost;
}
this.est_cost = estcost;
return estcost;
}
}
/**
* This function estimates the cost of a rule, which is used for sorting the rules for cube
* pruning. The estimated cost is basically the set of precomputable features (features listed
* along with the rule in the grammar file) along with any other estimates that other features
* would like to contribute (e.g., a language model estimate). This cost will be a lower bound on
* the rule's actual cost.
*
* <p>The value of this function is used only for sorting the rules. When the rule is later
* applied in context to particular hypernodes, the rule's actual cost is computed.
*
* @param models the list of models available to the decoder
* @return estimated cost of the rule
*/
@Override
public final float estimateRuleCost(List<FeatureFunction> models) {
if (null == models) return 0.0f;
if (this.estimatedCost <= Float.NEGATIVE_INFINITY) {
this.estimatedCost = 0.0f; // weights.innerProduct(computeFeatures());
// StringBuilder sb = new StringBuilder("estimateRuleCost(" + toString() + ")");
for (FeatureFunction ff : models) {
this.estimatedCost -= ff.estimateCost(this, -1);
// sb.append(String.format(" %s: %.3f", ff.getClass().getSimpleName(),
// -ff.estimateCost(this, -1)));
}
// sb.append(String.format(" ||| total=%.5f",this.estimatedCost));
// System.err.println(sb.toString());
}
return estimatedCost;
}