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com.hfg xml, html, svg, and bioinformatics utility library
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package com.hfg.math;

import java.util.Collection;
import java.util.Map;


//------------------------------------------------------------------------------
/**
 Lightweight sample statistics. The individual values are not retained.

 @author J. Alex Taylor, hairyfatguy.com
 */
//------------------------------------------------------------------------------
// com.hfg XML/HTML Coding Library
//
// This library is free software; you can redistribute it and/or
// modify it under the terms of the GNU Lesser General Public
// License as published by the Free Software Foundation; either
// version 2.1 of the License, or (at your option) any later version.
//
// This library is distributed in the hope that it will be useful,
// but WITHOUT ANY WARRANTY; without even the implied warranty of
// MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU
// Lesser General Public License for more details.
//
// You should have received a copy of the GNU Lesser General Public
// License along with this library; if not, write to the Free Software
// Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA
//
// J. Alex Taylor, President, Founder, CEO, COO, CFO, OOPS hairyfatguy.com
// [email protected]
//------------------------------------------------------------------------------

public class SimpleSampleStats
{

   //**************************************************************************
   // PRIVATE FIELDS
   //**************************************************************************

   private long     mSampleSize;
   private double   mSumX;
   private double   mSumX2;
   private Double   mMax;
   private Double   mMin;


   //**************************************************************************
   // PUBLIC FUNCTIONS
   //**************************************************************************

   //--------------------------------------------------------------------------
   public void add(SimpleSampleStats inStats)
   {
      mSampleSize += inStats.mSampleSize;
      mSumX       += inStats.mSumX;
      mSumX2      += inStats.mSumX2;
      if (inStats.mMax > mMax) mMax = inStats.mMax;
      if (inStats.mMin < mMin) mMin = inStats.mMin;
   }

   //--------------------------------------------------------------------------
   public void addAll(Collection inValues)
   {
      for (Number value : inValues)
      {
         add(value.doubleValue());
      }
   }

   //--------------------------------------------------------------------------
   public void addAll(int[] inValues)
   {
      for (double value : inValues)
      {
         add(value);
      }
   }

   //--------------------------------------------------------------------------
   public void addAll(double[] inValues)
   {
      for (double value : inValues)
      {
         add(value);
      }
   }

   //--------------------------------------------------------------------------
   public void addAll(Map inValueCountMap)
   {
      for (Number value : inValueCountMap.keySet())
      {
         Integer count = inValueCountMap.get(value);
         if (count != null
               && count != 0)
         {
            double doubleValue = value.doubleValue();

            if (null == mMin
                || doubleValue < mMin)
            {
               mMin = doubleValue;
            }

            if (null == mMax
                  || doubleValue > mMax)
            {
               mMax = doubleValue;
            }

            mSampleSize += count;
            mSumX  += doubleValue * count;
            mSumX2 += (doubleValue * doubleValue) * count;
         }
      }
   }

   //--------------------------------------------------------------------------
   public void add(Number inValue)
   {
      add(inValue.doubleValue());
   }

   //--------------------------------------------------------------------------
   public void add(int inValue)
   {
      add((double) inValue);
   }

   //--------------------------------------------------------------------------
   public void add(double inValue)
   {
      if (null == mMin
          || inValue < mMin)
      {
         mMin = inValue;
      }

      if (null == mMax
          || inValue > mMax)
      {
         mMax = inValue;
      }

      mSampleSize++;
      mSumX  += inValue;
      mSumX2 += (inValue * inValue);
   }

   //--------------------------------------------------------------------------
   public void clear()
   {
      mSampleSize = 0;
      mSumX  = 0;
      mSumX2 = 0;
      mMax = null;
      mMin = null;
   }

   //--------------------------------------------------------------------------
   public double getMin()
   {
      return mMin;
   }

   //--------------------------------------------------------------------------
   public double getMax()
   {
      return mMax;
   }

   //--------------------------------------------------------------------------
   public double getMean()
   {
      return mSumX / mSampleSize;
   }

   //--------------------------------------------------------------------------
   public long getSampleSize()
   {
      return mSampleSize;
   }

   //--------------------------------------------------------------------------
   public double getSampleStandardDeviation()
   {
      // The preferred equation is StdDev = Sqrt(Sum(X[i] - Xmean)^2 / (N-1) )
      // but since we don't keep all the values in this version and can't calculate the
      // mean until the end, we need to use this somewhat less precise version:
      // StdDev = Sqrt( (N SumX2 - SumX^2) / (N (N-1)) )
      return Math.sqrt((mSampleSize * mSumX2 - (mSumX * mSumX))/ (mSampleSize * (mSampleSize - 1)));
   }

   //--------------------------------------------------------------------------
   public StandardNormalDistribution getStandardNormalDistribution()
   {
      return new StandardNormalDistribution().setMean(getMean()).setSampleStandardDeviation(getSampleStandardDeviation());
   }




}