Examples of hep.aida.bin.DynamicBin1D

hep.aida.bin.DynamicBin1D
1-dimensional rebinnable bin holding double elements; Efficiently computes advanced statistics of data sequences. Technically speaking, a multiset (or bag) with efficient statistics operations defined upon. First see the package summary and javadoc tree view to get the broad picture.
The data filled into a DynamicBin1D is internally preserved in the bin. As a consequence this bin can compute more than only basic statistics. On the other hand side, if you add huge amounts of elements, you may run out of memory (each element takes 8 bytes). If this drawbacks matter, consider to use {@link StaticBin1D}, which overcomes them at the expense of limited functionality.
This class is fully thread safe (all public methods are synchronized). Thus, you can have one or more threads adding to the bin as well as one or more threads reading and viewing the statistics of the bin while it is filled. For high performance, add data in large chunks (buffers) via method addAllOf rather than piecewise via method add.
If your favourite statistics measure is not directly provided by this class, check out {@link cern.jet.stat.Descriptive} in combination with methods {@link #elements()} and {@link #sortedElements()}.
Implementation: Lazy evaluation, caching, incremental maintainance. @see cern.jet.stat.Descriptive @author wolfgang.hoschek@cern.ch @version 0.9, 03-Jul-99

             rand,
             false,
             false,
             2);


  DynamicBin1D dbin = new DynamicBin1D();
  
  /*
   * Use a new random number generator to generate numExamples
   * random gaussians, and add them to the QuantileBin1D
   */
  Uniform dataRand = new Uniform(new DRand(7757));
  for (int i = 1; i <= numExamples; i++) {
    double gauss = dataRand.nextDouble();
      qAccum.add(gauss);
      dbin.add(gauss);
  }
  
  /*
   * print out the percentiles
   */
  DecimalFormat fmt = new DecimalFormat("0.00");
  System.out.println();
  //int step = 1;
  int step = 10;
  for (int i = 1; i < 100; ) {
      double percent = ((double)i) * 0.01;
      double quantile = qAccum.quantile(percent);
      System.out.println(fmt.format(percent) + "  " + quantile + ",  " + dbin.quantile(percent)+ ",  " + (dbin.quantile(percent)-quantile));
      i = i + step;
  }
}

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 * @see Formatter
 * @see hep.aida.bin.BinFunction1D
 * @see hep.aida.bin.BinFunctions1D
 */
public static DoubleMatrix2D aggregate(DoubleMatrix2D matrix, hep.aida.bin.BinFunction1D[] aggr, DoubleMatrix2D result) {
  DynamicBin1D bin = new DynamicBin1D();
  double[] elements = new double[matrix.rows()];
  cern.colt.list.DoubleArrayList values = new cern.colt.list.DoubleArrayList(elements);
  for (int column=matrix.columns(); --column >= 0; ) {
    matrix.viewColumn(column).toArray(elements); // copy column into values
    bin.clear();
    bin.addAllOf(values);
    for (int i=aggr.length; --i >= 0; ) {
      result.set(i, column, aggr[i].apply(bin));
    }
  }
  return result;

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@param vector the vector to analyze.
@return a bin holding the statistics measures of the vector.
*/
public static DynamicBin1D bin(DoubleMatrix1D vector) {
  DynamicBin1D bin = new DynamicBin1D();
  bin.addAllOf(DoubleFactory1D.dense.toList(vector));
  return bin;
}

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 * @see Formatter
 * @see hep.aida.bin.BinFunction1D
 * @see hep.aida.bin.BinFunctions1D
 */
public static DoubleMatrix2D aggregate(DoubleMatrix2D matrix, hep.aida.bin.BinFunction1D[] aggr, DoubleMatrix2D result) {
  DynamicBin1D bin = new DynamicBin1D();
  double[] elements = new double[matrix.rows()];
  cern.colt.list.DoubleArrayList values = new cern.colt.list.DoubleArrayList(elements);
  for (int column=matrix.columns(); --column >= 0; ) {
    matrix.viewColumn(column).toArray(elements); // copy column into values
    bin.clear();
    bin.addAllOf(values);
    for (int i=aggr.length; --i >= 0; ) {
      result.set(i, column, aggr[i].apply(bin));
    }
  }
  return result;

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@param vector the vector to analyze.
@return a bin holding the statistics measures of the vector.
*/
public static DynamicBin1D bin(DoubleMatrix1D vector) {
  DynamicBin1D bin = new DynamicBin1D();
  bin.addAllOf(DoubleFactory1D.dense.toList(vector));
  return bin;
}

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             rand,
             false,
             false,
             2);


  DynamicBin1D dbin = new DynamicBin1D();
  
  /*
   * Use a new random number generator to generate numExamples
   * random gaussians, and add them to the QuantileBin1D
   */
  Uniform dataRand = new Uniform(new DRand(7757));
  for (int i = 1; i <= numExamples; i++) {
    double gauss = dataRand.nextDouble();
      qAccum.add(gauss);
      dbin.add(gauss);
  }
  
  /*
   * print out the percentiles
   */
  DecimalFormat fmt = new DecimalFormat("0.00");
  System.out.println();
  //int step = 1;
  int step = 10;
  for (int i = 1; i < 100; ) {
      double percent = ((double)i) * 0.01;
      double quantile = qAccum.quantile(percent);
      System.out.println(fmt.format(percent) + "  " + quantile + ",  " + dbin.quantile(percent)+ ",  " + (dbin.quantile(percent)-quantile));
      i = i + step;
  }
}

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cern.colt.list.DoubleArrayList

cern.colt.list.IntArrayList

cern.colt.matrix.doublealgo.Statistic

cern.jet.stat.quantile.Quantile1Test

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