package joshua.discriminative.semiring_parsing;

public class ExpectationSemiringVector implements CompositeSemiring{
//	TODO: assuming the following are always in *log* semiring
	private double logProb;
	
	//TODO: assuming the following are always in *real* semiring
	private SignedValue[] factor1;
	
	int vectorSize;
	
	public ExpectationSemiringVector(int vectorSize_){
		vectorSize= vectorSize_;
		factor1 = new SignedValue[vectorSize];
		for(int i=0; i<factor1.length; i++)
			factor1[i] = new SignedValue();
	}
	
	public ExpectationSemiringVector(double logProb_, SignedValue[] factor1_){
		logProb = logProb_;
		factor1 = factor1_;
		vectorSize = factor1.length;
	}
		
	public void setToZero(AtomicSemiring atomic){
		logProb = atomic.ATOMIC_ZERO_IN_SEMIRING;
		for(int i=0; i<vectorSize; i++){
			factor1[i].setZero();
		}
	}
	
	public void setToOne(AtomicSemiring atomic){
		logProb = atomic.ATOMIC_ONE_IN_SEMIRING;
		
		/**Note that factor should always set as zero. 
		 * For example, when the factor is expected length, it should start from zero
		 * */
		for(int i=0; i<vectorSize; i++){
			factor1[i].setZero();
		}
	}	

	public void  add(CompositeSemiring b, AtomicSemiring atomic){
		ExpectationSemiringVector b2 = (ExpectationSemiringVector)b;
		this.logProb    = atomic.add_in_atomic_semiring(this.logProb, b2.logProb);
		for(int i=0; i<vectorSize; i++){
			this.factor1[i] = SignedValue.add(this.factor1[i], b2.factor1[i]);
		}
	}
	
	public void multi(CompositeSemiring b, AtomicSemiring atomic){
		ExpectationSemiringVector b2 = (ExpectationSemiringVector)b;
			
		for(int i=0; i<vectorSize; i++){			
			//this.factor1[i] = Math.exp(oldLogProb)* b2.factor1[i] + Math.exp(b2.logProb) * oldFactor1[i];
			this.factor1[i] = SignedValue.add(
					SignedValue.multi(this.logProb, b2.factor1[i]),
					SignedValue.multi(b2.logProb,  this.factor1[i])
				   );
		}
		
		this.logProb = atomic.multi_in_atomic_semiring(this.logProb, b2.logProb);
	}
	
	
	public void normalizeFactors(){
		/**we should not normalize the probability at each intermediate node
		 * because our model is a global model???
		 */
		
		/**originallly, the factor value is \sum_x p(x).v(x), where p(x) is not normalized, meaning \sum_x p(x)!=1;
		 * we need to normalize p(x) by divide out Math.exp(prob)
		 * */
		for(int i=0; i<vectorSize; i++){
			//this.factor1[i] = factor1[i]/Math.exp(logProb);
			this.factor1[i] = SignedValue.multi(-logProb, this.factor1[i]);
		}
	}
	
	public void printInfor(){
		System.out.println("prob: " + logProb );
		System.out.print("factor1:");
		for(int i=0; i<vectorSize; i++){
			System.out.print(" " + factor1[i].convertRealValue());
		}
		System.out.print("\n");
	}
	
	
	public void printInfor2(){
		SignedValue[] true_factor1 = new SignedValue[vectorSize];
		for(int i=0; i<vectorSize; i++){
			true_factor1[i] = SignedValue.multi(-logProb, this.factor1[i]);
		}
		System.out.println("prob: " + logProb + "; factor1: " + factor1 + "; factor2: " + factor1);		
		System.out.println("true factor1: " + true_factor1 + "; true factor2: " + true_factor1);
	}
	
	
	public double getLogProb(){
		return logProb;
	}
	
	public SignedValue[] getFactor1(){
		return factor1;
	}
	
}