package dukeScrobbler;

import java.io.File;
import java.io.FileNotFoundException;
import java.io.FileOutputStream;
import java.io.PrintStream;
import java.util.Collection;
import java.util.Iterator;

import com.planetj.taste.common.TasteException;
import com.planetj.taste.correlation.UserCorrelation;
import com.planetj.taste.impl.correlation.AveragingPreferenceInferrer;
import com.planetj.taste.impl.correlation.CosineMeasureCorrelation;
import com.planetj.taste.impl.correlation.PearsonCorrelation;
import com.planetj.taste.impl.correlation.SpearmanCorrelation;
import com.planetj.taste.impl.neighborhood.NearestNUserNeighborhood;
import com.planetj.taste.impl.neighborhood.ThresholdUserNeighborhood;
import com.planetj.taste.model.DataModel;
import com.planetj.taste.model.Item;
import com.planetj.taste.model.Preference;
import com.planetj.taste.model.User;
import com.planetj.taste.neighborhood.UserNeighborhood;

/**
 * gdfMaker takes a dataModel and writes a GUESS .gdf file
 * based on a nearest user neighborhood 
 * using 3 neighbors and cosine correlation
 * 
 * @author Ben Spain
 *
 */
public  class gdfMaker {
	
	
	public static void makeGDF(DataModel model, File f) throws TasteException{
		PrintStream output = null;
        try {
            output = new PrintStream(new FileOutputStream(f.toString(), true)); 
            
        } catch (FileNotFoundException e) {
            
           System.out.println(e);
        }
        // GDF node header
        output.println("nodedef> name,id INT default 0");
		
        Iterator it = model.getUsers().iterator();
		
        String toPrint = "";
        int id = 0;
        
        
        while(it.hasNext()){
        	
        	toPrint = "";
        	User u = (User)it.next();
        	
        	toPrint +=  u.getID();
        	toPrint += "," + id;
        	id++;
        	output.println(toPrint);
        }
        // GDF edge header
        output.println("edgedef> node1,node2,cosine DOUBLE default 0,pearson DOUBLE default 0,spearman DOUBLE default 0");
        // Associate all 3 correlation values for each edge
        UserCorrelation cosineCorr = new CosineMeasureCorrelation(model);
        UserCorrelation pCorr = new PearsonCorrelation(model);
        UserCorrelation sCorr = new SpearmanCorrelation(model);
        cosineCorr.setPreferenceInferrer(new AveragingPreferenceInferrer());
        pCorr.setPreferenceInferrer(new AveragingPreferenceInferrer());
        sCorr.setPreferenceInferrer(new AveragingPreferenceInferrer());
        UserNeighborhood neighborhood = new NearestNUserNeighborhood(3, cosineCorr, model);
        
        Iterator it3 = model.getUsers().iterator();
        
        while(it3.hasNext()){
        	User u = (User)it3.next();
        	Collection c = neighborhood.getUserNeighborhood(u.getID());
        	Iterator it4 = c.iterator();
        	while(it4.hasNext()){
        		
        		toPrint = "";
        		User n = (User)it4.next();
        		toPrint += u.getID() + "," +n.getID()+",";
        		toPrint += cosineCorr.userCorrelation(u,n) + ",";
        		toPrint += pCorr.userCorrelation(u,n) + ",";
        		toPrint += sCorr.userCorrelation(u,n);
        		System.out.println(toPrint);
        		output.println(toPrint);
        	}
        }
      
	}
}