package mpqa_seq_reranker;

import se.lth.cs.nlp.nlputils.ml_long.*;

public class RerankingDefinition extends ProblemDefinition<NBestRepresentation, Integer> {

	private static final long serialVersionUID = 0;

	public double cost(NBestRepresentation x, Integer y, Integer ybar) {
		return x.costs[ybar] - x.costs[y];
	}
	
    public Integer argmax(NBestRepresentation x, Model model, SparseVector sv, double[] result) {
    	double maxScore = Double.NEGATIVE_INFINITY;
    	int max = -1;
    	for(int i = 0; i < x.reps.length; i++) {
    		double score = model.score(x.reps[i]);
    		if(score > maxScore) {
    			maxScore = score;
    			max = i;
    		}
    	}
    	result[0] = maxScore;
    	encode(x, max, sv);
    	return max;
    }
    
    // TODO för svmstruct borde det nog inte vara sqrt
    private static final boolean SQRT = true; // 110407: f->t //false;
    public Integer maxLoss(NBestRepresentation x, Integer y, Model model, SparseVector sv, double[] result) {
    	double maxScore = Double.NEGATIVE_INFINITY;
    	int max = -1;
    	for(int i = 0; i < x.reps.length; i++) {
    		double score = model.score(x.reps[i]);

    		if(SQRT)
    			score += Math.sqrt(x.costs[i] - x.costs[y]);
    		else
    			score += x.costs[i] - x.costs[y];

    		if(score > maxScore) {
    			maxScore = score;
    			max = i;
    		}
    	}
    	result[0] = model.score(x.reps[max]);
    	result[1] = x.costs[max] - x.costs[y];
    	encode(x, max, sv);
    	return max;
    }
    
    public void encode(NBestRepresentation x, Integer y, SparseVector sv) {
    	sv.clear();
    	sv.putAll(x.reps[y]);
    }

    public void setMaxFeatureIndex(int n) {
    	System.out.println("Warning: setMaxFeatureIndex ignored");
    }
    
}