package objects;

import java.util.Vector;

import utilities.*;

public class CorrelationNet extends Net {

	public double[][][] corr;
	
	
	public CorrelationNet(String graph_n, String graph_n2, int agents, int num_deceiver) {
		super(graph_n, graph_n2, agents, num_deceiver);
		type = constant.TYPE_CORRELATIONNET;
	}
	
	public CorrelationNet(Net net) {
		super(net);
		type = constant.TYPE_CORRELATIONNET;
		
	}
	
	
	public void updateNet() {
		updateCorrelation();
		updateNet(corr, constant.threshold);
	}
	
	
	
	
	/*
	 * give more weight to certain values
	 */
	public void updateCorrelation () {
    	
    	corr = new double[actors.length][actors.length][actors[0].getTrainValues().length];
    
    	//double[][][] data2 = getNewCorrelationData2(data, data) ;
    
    	//double[][] avrg = getAverage(data2);
    	for (int j=0; j<actors.length; j++) {
    		double[][] jvalues = actors[j].getTrainValues();
    		if (jvalues==null) {
    			System.err.println("in CorrelationNet: train stories are not inferred");
    			System.exit(-1);
    		}
    		double[] javerage = utility.calcuAverage2D(jvalues);
    		for (int k=0; k<jvalues.length; k++) {
    			for (int l=0; l<actors.length; l++) {
    				if (j==l) {
    					corr[j][l][k]=1;
    					continue;
    				}
    				double[][] lvalues = actors[l].getTrainValues();
    				double[] laverage = utility.calcuAverage2D(lvalues);
    				if (j==0) {
    					System.out.println("agent 0 value "+k+" : avr="+javerage[k]);
    					utility.printArray(jvalues[k], true);
    					
    					System.out.println("agent "+l+" value "+k+" : avr="+laverage[k]);
    					utility.printArray(lvalues[k], true);
    				}
    				//corr[j][l][k]=0;
    				double up=0.0;
    				double downl=0.0;
    				double downr=0.0;
    				for (int i=0; i<jvalues[k].length; i++) {
    					//System.out.println(k+":"+i);
    					if (jvalues[k][i]<=-1 || lvalues[k][i]<=-1) continue;
    					double diffX=jvalues[k][i]-javerage[k];
    					double diffY=lvalues[k][i]-laverage[k];
    					up+=diffX*diffY;
    					downl+=diffX*diffX;
    					downr+=diffY*diffY;
    					
    					//more weight if value is certain
    					if ((jvalues[k][i]==0||jvalues[k][i]==1)&&(lvalues[k][i]==0||lvalues[k][i]==1)) {
    						up+=diffX*diffY;
        					downl+=diffX*diffX;
        					downr+=diffY*diffY;
    					}
    					
    				}
    				if (downl==0||downr==0) {
    					
    					corr[j][l][k]=0;
    				}
    				else
    					corr[j][l][k]=up/(Math.sqrt(downl)*Math.sqrt(downr));
    				if (j==0) 
    				System.out.println("correlation="+corr[j][l][k]);
    			}
    		}
    	}
    	
    	//return corr;
    }
}