standard deviation for the slope of the linear fit

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        private long numberOfElements;
        private BigDecimal sum;
        private BigDecimal sumSquare;
  
        public PartialResult(long numberOfElements, BigDecimal sum, BigDecimal sumSquare) {
            this.numberOfElements = numberOfElements;
            this.sum = sum;
            this.sumSquare = sumSquare;
        }
    }
  
    public static class ParallelPart extends StdDeviation implements EntryAggregator {
  
       private ParallelPart(ValueExtractor extractor) {
          super(extractor);
       }
    }
  
    private ValueExtractor extractor;
  
    private transient boolean notParallel;
  
    public StdDeviation(ValueExtractor extractor) {
      this.extractor = extractor;
      this.notParallel = true;
    }
  
    public Object aggregate(Set entries) {
       ValueExtractor extractor = this.extractor;
       BigDecimal sum = 0;
       BigDecimal sumSquare = 0;
       long num = 0;
       for (InvocableMap.Entry entry : (Set) partialResults) {
          sum = sum.add(partialResult.sum);
          sumSquare = sumSquare.add(partialResult.sumSquare);
          num += partialResult.numberOfElements;
       }
  
       return calculateStdDeviation(num, sum, sumSquare);
    }
  
    public EntryAggregator getParallelAggregator() {
  
       // you could return this, instead of ParallelPart
       //
       // return this;
  
       return new ParallelPart(extractor);
    }
  
    private static Number calculateStdDeviation(long num, BigDecimal sum, BigDecimal sumSquare) {
       BigDecimal partRes = sumSquare.multiply(new BigDecimal(num)).subtract(sum.multiply(sum));
  
       // you can replace the square rooting of a double value with a proper implementation of square root for BigDecimals,
       // which I omitted for the sake of brevity, and then you can do BigDecimal division (make sure you provide a MathContext)
       return Math.sqrt(partRes.doubleValue())/num;
    }
  
    public static Number execute(NamedCache cache, ValueExtractor extractor) {
       return (Number) cache.aggregateAll(AlwaysFilter.INSTANCE, new StdDeviation(extractor));
    }
  }
  

  There is actually no reason for the class ParallelPart to exist. I put it here to illustrate that there can be a different aggregator instance to the ParallelAware aggregator optimized to run on the side of the storage, but you could just "this" return of the getParallelAggregator() method, it wouldn't make a difference for this implementation of the aggregator.

  You can replace the rooting square a double value with a good implementation of the square root of BigDecimals, which I omitted for brevity, and then you can make division BigDecimal (make sure that you provide a MathContext)

  I also omitted the ExternalizableLite and PortableObject implementations (and necessary parameterless constructors) for brevity of the ParallelPart and PartialResult classes.

  In addition, you may need some mojo if you want to use a PofExtractor instead of a regular Value Extractor to avoid the deserialization of the cached value.

  You can perform the aggregation with the execute method, passing the cache altogether on the Extractor which extracts the value as a BigDecimal value cached.

  Best regards

  Robert

  Published by: robvarga on March 12, 2010 11:36

  Published by: robvarga on March 12, 2010 11:43

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