import java.util.*;
import java.io.*;

public class DancingWithTheGooglers
{
    public DancingWithTheGooglers()
    {
        Scanner inFile = null;
        try
        {
            inFile = new Scanner(new File("inputB.txt"));
        }
        catch(Exception e)
        {
            System.out.println("Problem opening input file. Exiting...");
			System.exit(0);
        }
        int t = inFile.nextInt();
        
        int [] results = new int [t];
        DancingWithTheGooglersMax [] rnc = new DancingWithTheGooglersMax[t];
        
        int i, j;
        int [] scores;
        int s, p;
        for(i = 0; i < t; i++)
        {
            scores = new int [inFile.nextInt()];
            s = inFile.nextInt();
            p = inFile.nextInt();
            for(j = 0; j < scores.length; j++)
                scores[j] = inFile.nextInt();
            
            rnc[i] = new DancingWithTheGooglersMax(i, scores, s, p, results);
            rnc[i].start();
        }
        inFile.close();
        
        boolean done = false;
        
        while(!done)
        {
            done = true;
            for(i = 0; i < t; i++)
                if(rnc[i].isAlive())
                {
                    done = false;
                    break;
                }
        }
        
        PrintWriter outFile = null;
        try
        {
            outFile = new PrintWriter(new File("outputB.txt"));
        }
        catch(Exception e)
        {
            System.out.println("Problem opening output file. Exiting...");
			System.exit(0);
        }
        
        for(i = 0; i < results.length; i++)
            outFile.printf("Case #%d: %d\n", i+1, results[i]);
        outFile.close();
    }
    
    public static void main(String [] args)
    {
        new DancingWithTheGooglers();
    }
    
    private class DancingWithTheGooglersMax extends Thread
    {
        int id;
        int s, p;
        int [] results, scores;
        
        public DancingWithTheGooglersMax(int i, int [] aScores, int aS, int aP,int [] res)
        {
            id = i;
            s = aS;
            p = aP;
            results = res;
            scores = aScores;
        }
        
        public void run()
        {
            int a = 0, b = 0;
            
            if(p == 0)
                results[id] = scores.length;
            else if(p == 1)
            {
                int y;
                y = 3*p - 3;
                
                for(int i = 0; i < scores.length; i++)
                    if(scores[i] > y)
                        a++;
                    else if(scores[i] > 0)
                        b++;
                
                b = Math.min(b, s);
                results[id] = a+b;
            }
            else
            {
                int y, z;
                y = 3*p - 3;
                z = 3*p - 5;
                
                for(int i = 0; i < scores.length; i++)
                    if(scores[i] > y)
                        a++;
                    else if(scores[i] > z)
                        b++;
                
                b = Math.min(b, s);
                results[id] = a+b;
            }
        }
    }
}
package fixjava;

import java.util.concurrent.Callable;
import java.util.concurrent.Future;

import fixjava.ParallelWorkQueue.CallableFactory;

public class ArrayStats {
	public final double[][] data;
	public final int n, k;

	private double[][] centered;
	private double[] mean;
	private double[][] cov;
	private double[] stddev = null;
	private double[][] corr = null;

	/** Calculate covariance matrix */
	public ArrayStats(double[][] data) {
		if (data == null || data.length < 2 || data[0].length < 2)
			throw new IllegalArgumentException("Invalid data");
		this.data = data;
		this.n = data.length;
		this.k = data[0].length;
	}

	/** Compute mean for each dimension */
	public double[] mean() {
		if (mean == null) {
			mean = new double[k];
			for (int dim = 0; dim < k; dim++) {
				double tot = 0;
				for (int pt = 0; pt < n; pt++)
					tot += data[pt][dim];
				mean[dim] = tot / n;
			}
		}
		return mean;
	}

	/** Compute stddev for each dimension */
	public double[] stddev() {
		if (stddev == null) {
			// Make sure means are computed
			mean();

			stddev = new double[k];
			for (int dim = 0; dim < k; dim++) {
				for (int pt = 0; pt < n; pt++) {
					double diff = data[pt][dim] - mean[dim];
					stddev[dim] += diff * diff;
				}
				stddev[dim] = Math.sqrt(stddev[dim] / n);
			}
		}
		return stddev;
	}

	/** Re-center data by subtracting mean from each dimension */
	public double[][] centered() {
		if (centered == null) {
			// Make sure means are computed
			mean();

			centered = new double[n][k];
			for (int pt = 0; pt < n; pt++)
				for (int dim = 0; dim < k; dim++)
					centered[pt][dim] = data[pt][dim] - mean[dim];
		}
		return centered;
	}

	/** Compute covariance matrix */
	public double[][] cov() {
		if (cov == null) {
			// Make sure centered version of data is computed
			centered();

			// Work on transposed array for cache coherence
			final double[][] centeredT = ArrayUtils.transpose(centered);

			cov = new double[k][k];

			try {
				//final DecimalFormat formatPercent1dp = new DecimalFormat("0.0%");
				for (Future<Void> res : new ParallelWorkQueue<Integer, Void>(/* numThreads = */25,
						ParallelWorkQueue.makeIntRangeIterable(k), new CallableFactory<Integer, Void>() {
							//IntegerMutable counter = new IntegerMutable();

							@Override
							public Callable<Void> newInstance(final Integer ii) {
								return new Callable<Void>() {
									int dim1 = ii.intValue();
									double[][] centeredTLocal = centeredT;
									double[][] covLocal = cov;

									@Override
									public Void call() {
										for (int dim2 = dim1; dim2 < k; dim2++) {
											double numer = 0.0;
											for (int pt = 0; pt < n; pt++)
												numer += centeredTLocal[dim1][pt] * centeredTLocal[dim2][pt];
											int denom = n - 1;
											covLocal[dim1][dim2] = covLocal[dim2][dim1] = denom == 0 ? 0 : numer / denom;
										}
										//										int count = counter.increment();
										//										if (count % 10 == 0)
										//											System.out.println(formatPercent1dp.format((count / (float) (k - 1))));
										return null;
									}
								};
							}
						}))
					// Barricade
					res.get();
			} catch (Exception e) {
				e.printStackTrace();
				System.exit(1);
			}
		}
		return cov;
	}

	/** Compute correlation matrix */
	public double[][] corr() {
		if (corr == null) {
			// Make sure per-dim stddevs and covariance matrix are computed
			stddev();
			cov();

			corr = new double[k][k];
			for (int dim1 = 0; dim1 < k; dim1++) {
				for (int dim2 = dim1; dim2 < k; dim2++) {
					double prod = stddev[dim1] * stddev[dim2];
					corr[dim1][dim2] = corr[dim2][dim1] = prod == 0.0 ? 0.0 : cov[dim1][dim2] / prod;
				}
			}
		}
		return corr;
	}
}
