Examples of cern.colt.matrix.DoubleMatrix2D

cern.colt.matrix.DoubleMatrix2D
Abstract base class for 2-d matrices holding double elements. First see the package summary and javadoc tree view to get the broad picture.
A matrix has a number of rows and columns, which are assigned upon instance construction - The matrix's size is then rows()*columns(). Elements are accessed via [row,column] coordinates. Legal coordinates range from [0,0] to [rows()-1,columns()-1]. Any attempt to access an element at a coordinate column<0 || column>=columns() || row<0 || row>=rows() will throw an IndexOutOfBoundsException.
Note that this implementation is not synchronized. @author wolfgang.hoschek@cern.ch @version 1.0, 09/24/99

 * Demonstrates advanced sorting.
 * Sorts by sum of row.
 */
public static void zdemo1() {
  Sorting sort = quickSort;
  DoubleMatrix2D matrix = DoubleFactory2D.dense.descending(4,3);
  DoubleMatrix1DComparator comp = new DoubleMatrix1DComparator() {
    public int compare(DoubleMatrix1D a, DoubleMatrix1D b) {
      double as = a.zSum(); double bs = b.zSum();
      return as < bs ? -1 : as == bs ? 0 : 1;
    }

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  System.out.println("\n\n");
  System.out.print("now initializing... ");
  cern.colt.Timer timer = new cern.colt.Timer().start();
    
  final cern.jet.math.Functions F = cern.jet.math.Functions.functions;
  DoubleMatrix2D A = cern.colt.matrix.DoubleFactory2D.dense.make(rows,columns);
  A.assign(new cern.jet.random.engine.DRand()); // initialize randomly
  timer.stop().display();


  // also benchmark copying in its several implementation flavours
  DoubleMatrix2D B = A.like();
  timer.reset().start();
  System.out.print("now copying... ");
  B.assign(A);
  timer.stop().display();


  timer.reset().start();
  System.out.print("now copying subrange... ");
  B.viewPart(0,0,rows,columns).assign(A.viewPart(0,0,rows,columns));
  timer.stop().display();
  //System.out.println(A);


  timer.reset().start();
  System.out.print("now copying selected... ");
  B.viewSelection(null,null).assign(A.viewSelection(null,null));
  timer.stop().display();


  System.out.print("now sorting - quick version with precomputation... ");
  timer.reset().start();
  // THE QUICK VERSION (takes some 10 secs)

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    { 2,3,0 },
    { 1,0,0 },
    { 4,0,0 },
    { 2,0,0 }
  };
  DoubleMatrix2D A = DoubleFactory2D.dense.make(values);
  DoubleMatrix2D B,C;
  /*
  DoubleMatrix1DComparator comp = new DoubleMatrix1DComparator() {
    public int compare(DoubleMatrix1D a, DoubleMatrix1D b) {
      double as = a.zSum(); double bs = b.zSum();
      return as < bs ? -1 : as == bs ? 0 : 1;

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  System.out.println("\n\n");
  System.out.println("now initializing... ");
    
  final cern.jet.math.Functions F = cern.jet.math.Functions.functions;
  DoubleMatrix2D A = cern.colt.matrix.DoubleFactory2D.dense.make(rows,columns);
  A.assign(new cern.jet.random.engine.DRand()); // initialize randomly
  DoubleMatrix2D B = A.copy();


  double[] v1 = A.viewColumn(0).toArray();
  double[] v2 = A.viewColumn(0).toArray();
  System.out.print("now quick sorting... ");
  cern.colt.Timer timer = new cern.colt.Timer().start();

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        //if (true) {
        //if (!((k==1 && density >= 0.1 && size >=100) || (size>5000 && (k==0 || density>1.0E-4) ))) {
        if (!((k>0 && density >= 0.1 && size >=500) )) {
          double val = 0.5;
          function.A=null; function.B=null; function.C=null; function.D=null; // --> help gc before allocating new mem
          DoubleMatrix2D A = factory.sample(size,size,val,density);
          DoubleMatrix2D B = factory.sample(size,size,val,density);
          function.setParameters(A,B);
          A = null; B = null; // help gc
          double ops = function.operations();
          double secs = BenchmarkKernel.run(minSeconds,function);
          opsPerSec = (float) (ops / secs);

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protected static void runSpecial(double minSeconds, String title, Double2DProcedure function) {
  int[] sizes =        {10000};
  double[] densities = {0.00001};
  boolean[] sparses  = {true};
  
  DoubleMatrix2D timings = DoubleFactory2D.dense.make(sizes.length,4);
  cern.colt.Timer runTime = new cern.colt.Timer().start();
  for (int i=0; i<sizes.length; i++) {
    int size = sizes[i];
    double density = densities[i];
    boolean sparse = sparses[i];
    final DoubleFactory2D factory = (sparse ? DoubleFactory2D.sparse : DoubleFactory2D.dense);
    System.out.print("\n@"); 


    System.out.print("x");
    double val = 0.5;
    function.A=null; function.B=null; function.C=null; function.D=null; // --> help gc before allocating new mem
    DoubleMatrix2D A = factory.sample(size,size,val,density);
    DoubleMatrix2D B = factory.sample(size,size,val,density);
    function.setParameters(A,B);
    A = null; B = null; // help gc
    float secs = BenchmarkKernel.run(minSeconds,function);
    double ops = function.operations();
    float opsPerSec = (float) (ops / secs);

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 * @return the covariance matrix (<tt>n x n, n=matrix.columns</tt>).
 */
public static DoubleMatrix2D covariance(DoubleMatrix2D matrix) {
  int rows = matrix.rows();
  int columns = matrix.columns();
  DoubleMatrix2D covariance = new cern.colt.matrix.impl.DenseDoubleMatrix2D(columns,columns);
  
  double[] sums = new double[columns];
  DoubleMatrix1D[] cols = new DoubleMatrix1D[columns];
  for (int i=columns; --i >= 0; ) {
    cols[i] = matrix.viewColumn(i);
    sums[i] = cols[i].zSum();
  }


  for (int i=columns; --i >= 0; ) {
    for (int j=i+1; --j >= 0; ) {
      double sumOfProducts = cols[i].zDotProduct(cols[j]);
      double cov = (sumOfProducts - sums[i]*sums[j]/rows) / rows;
      covariance.setQuick(i,j,cov);
      covariance.setQuick(j,i,cov); // symmetric
    }
  }
  return covariance;  
}

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  { 2, 4, 6 },
  { 3, 6, 9 },
  { 4, -8, -10 }
};
DoubleFactory2D factory = DoubleFactory2D.dense;
DoubleMatrix2D A = factory.make(values);
System.out.println("\n\nmatrix="+A);
System.out.println("\ncovar1="+covariance(A));
//System.out.println(correlation(covariance(A)));
//System.out.println(distance(A,EUCLID));

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 * Demonstrates usage of this class.
 */
public static void demo2(int rows, int columns, boolean print) {
System.out.println("\n\ninitializing...");
DoubleFactory2D factory = DoubleFactory2D.dense;
DoubleMatrix2D A = factory.ascending(rows,columns);
//double value = 1;
//DoubleMatrix2D A = factory.make(rows,columns);
//A.assign(value);


System.out.println("benchmarking correlation...");


cern.colt.Timer timer = new cern.colt.Timer().start();
DoubleMatrix2D corr = correlation(covariance(A));
timer.stop().display();


if (print) {
  System.out.println("printing result...");
  System.out.println(corr);

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  {-1.1341542,  0.20388430}
};


System.out.println("\n\ninitializing...");
DoubleFactory2D factory = DoubleFactory2D.dense;
DoubleMatrix2D A = factory.make(values).viewDice();


System.out.println("\nA="+A.viewDice());
System.out.println("\ndist="+distance(A,norm).viewDice());
}

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