source: branches/HeuristicLab.Problems.GaussianProcessTuning/ILNumerics.2.14.4735.573/Functions/statistics/nansum.cs @ 11194

///
///    This file is part of ILNumerics Community Edition.
///
///    ILNumerics Community Edition - high performance computing for applications.
///    Copyright (C) 2006 - 2012 Haymo Kutschbach, http://ilnumerics.net
///
///    ILNumerics Community Edition is free software: you can redistribute it and/or modify
///    it under the terms of the GNU General Public License version 3 as published by
///    the Free Software Foundation.
///
///    ILNumerics Community Edition 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 General Public License for more details.
///
///    You should have received a copy of the GNU General Public License
///    along with ILNumerics Community Edition. See the file License.txt in the root
///    of your distribution package. If not, see <http://www.gnu.org/licenses/>.
///
///    In addition this software uses the following components and/or licenses: 
///
///    =================================================================================
///    The Open Toolkit Library License
///    
///    Copyright (c) 2006 - 2009 the Open Toolkit library.
///    
///    Permission is hereby granted, free of charge, to any person obtaining a copy
///    of this software and associated documentation files (the "Software"), to deal
///    in the Software without restriction, including without limitation the rights to 
///    use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of
///    the Software, and to permit persons to whom the Software is furnished to do
///    so, subject to the following conditions:
///
///    The above copyright notice and this permission notice shall be included in all
///    copies or substantial portions of the Software.
///
///    =================================================================================
///    

using System;
using System.Collections.Generic;
using System.Text;
using System.Threading; 
using ILNumerics.Storage;
using ILNumerics.Misc;
using ILNumerics.Exceptions;



namespace ILNumerics  {
  public partial class ILMath {

    /// <summary>
    /// Sum elements along first non singleton dimension ignoring NaN values
    /// </summary>
    /// <param name="A">Input array</param>
    /// <param name="dim">[Optional] Index of the dimension to operate along. If omitted operates along the first non singleton dimension (i.e. != 1).</param>
    /// <returns>Sum of elements along specified of first non singleton dimension, ignoring nan values</returns>
    /// <remarks><para>The array returned will have the same size as <paramref name="A"/>, with the specified or first non singleton dimension 
    /// reduced to the length 1. It will contain the sum of all elements along that dimension after removing NaN values respectively. </para>
    /// <para>If A contains an all NaN vector along <paramref name="dim"/> , 
    /// the resulting sum will be 0 - not NaN! This corresponds to the sum of an empty vector.</para></remarks> 
    public static ILRetArray<double> nansum(ILInArray<double> A, int dim = -1)
    {
      if (dim < 0)
        dim = A.Size.WorkingDimension();
      return nansum_internal(A, dim, false);
    }
    
    /// <summary>
    /// Depending on settings, calculate nansum or nanmean
    /// </summary>
    private static ILRetArray<double> nansum_internal (ILInArray<double> A, int dim, bool computeMean) {
      using (ILScope.Enter(A)) {
        if (dim >= A.Size.NumberOfDimensions)
           return A.C;
        
        if (A.IsScalar) {
          return array<double>(new double[1] { A.GetValue(0) }, 1, 1);
        }
        
        if (A.S[dim] == 1) return A.C;

        int[] newDims = A.S.ToIntArray();
        newDims[dim] = 1; 
        ILSize retDimension = new ILSize(newDims);

        if (A.IsEmpty)
          return empty<double>(retDimension);

        double[] retArr = ILMemoryPool.Pool.New<double>(retDimension.NumberOfElements);

        int inc = A.Size.SequentialIndexDistance(dim);
        int dimLen = A.Size[dim];
        int maxRuns = retDimension.NumberOfElements;
        int modHelp = A.Size.NumberOfElements - 1;
        int modOut = retDimension.NumberOfElements - 1;
        int incOut = retDimension.SequentialIndexDistance(dim); 
        int numelA = A.S.NumberOfElements;
        
        double[] aArray = A.GetArrayForRead();
        if (maxRuns == 1) {
          
          double tmp = 0;
          if (computeMean)
          {
            int cnt = 0;
            for (int j = 0; j < dimLen; j++)
            {
              if (!/*HC:inArr1*/double.IsNaN(aArray[j]))
              {
                tmp += aArray[j];
                cnt++;
              }
            }
            if (cnt == 0)
              retArr[0] = double.NaN;
            else
              retArr[0] = tmp / cnt;
          }
          else
          {
            for (int j = 0; j < dimLen; j++)
            {
              if (!/*HC:inArr1*/double.IsNaN(aArray[j]))
                tmp += aArray[j];
            }
            retArr[0] = tmp;
          }
        } else {
          #region may run parallel 
          int i = 0, workItemCount = Settings.s_maxNumberThreads, workItemLength, workerCount = 1;
          if (Settings.s_maxNumberThreads > 1 && maxRuns > 1 
            && numelA / 2 >= Settings.s_minParallelElement1Count) {

            if (maxRuns >= Settings.s_maxNumberThreads
              && numelA / Settings.s_maxNumberThreads > Settings.s_minParallelElement1Count) {
              workItemLength = maxRuns / workItemCount;
            } else {
              workItemLength = maxRuns / 2;
              workItemCount = 2;
            }
            
          } else {
            workItemLength = maxRuns;
            workItemCount = 1;
          }
          Action<object> action = (data) => {
            Tuple<int, int> range = (Tuple<int, int>)data;
            int from = range.Item1, to = range.Item2;
            for (int c = from; c < to; c++) {
              int pos = (int)(((long)dimLen * c * inc) % modHelp);
              long posOut = ((long)c * incOut);
              if (posOut > modOut)
                posOut = ((posOut - 1) % modOut) + 1;
              
              double tmp = 0;
              int end = pos + dimLen * inc;
              if (computeMean)
              {
                int cnt = 0;
                for (int j = pos; j < end; j += inc)
                {
                  if (!/*HC:inArr1*/double.IsNaN(aArray[j]))
                  { 
                    tmp += aArray[j];
                    cnt++;
                  }
                }
                if (cnt == 0)
                  retArr[posOut] = double.NaN;
                else
                  retArr[posOut] = tmp / cnt;
              }
              else
              {
                 
                for (int j = pos; j < end; j += inc)
                {
                  if (!/*HC:inArr1*/double.IsNaN(aArray[j]))
                    tmp += aArray[j];
                }
                retArr[posOut] = tmp;
              }
            }
            System.Threading.Interlocked.Decrement(ref workerCount);
          };
          for (; i < workItemCount - 1; i++) {
            Interlocked.Increment(ref workerCount);

            ILThreadPool.QueueUserWorkItem(i,action, Tuple.Create(i * workItemLength, (i + 1) * workItemLength));
          }
          action(Tuple.Create(i * workItemLength, maxRuns));
          ILThreadPool.Wait4Workers(ref workerCount); 
          #endregion
        }
        return array(retArr, newDims);
      }
    }

#region HYCALPER AUTO GENERATED CODE

    /// <summary>
    /// Sum elements along first non singleton dimension ignoring NaN values
    /// </summary>
    /// <param name="A">Input array</param>
    /// <param name="dim">[Optional] Index of the dimension to operate along. If omitted operates along the first non singleton dimension (i.e. != 1).</param>
    /// <returns>Sum of elements along specified of first non singleton dimension, ignoring nan values</returns>
    /// <remarks><para>The array returned will have the same size as <paramref name="A"/>, with the specified or first non singleton dimension 
    /// reduced to the length 1. It will contain the sum of all elements along that dimension after removing NaN values respectively. </para>
    /// <para>If A contains an all NaN vector along <paramref name="dim"/> , 
    /// the resulting sum will be 0 - not NaN! This corresponds to the sum of an empty vector.</para></remarks> 
    public static ILRetArray<fcomplex> nansum(ILInArray<fcomplex> A, int dim = -1)
    {
      if (dim < 0)
        dim = A.Size.WorkingDimension();
      return nansum_internal(A, dim, false);
    }
    
    /// <summary>
    /// Depending on settings, calculate nansum or nanmean
    /// </summary>
    private static ILRetArray<fcomplex> nansum_internal (ILInArray<fcomplex> A, int dim, bool computeMean) {
      using (ILScope.Enter(A)) {
        if (dim >= A.Size.NumberOfDimensions)
           return A.C;
        
        if (A.IsScalar) {
          return array<fcomplex>(new fcomplex[1] { A.GetValue(0) }, 1, 1);
        }
        
        if (A.S[dim] == 1) return A.C;

        int[] newDims = A.S.ToIntArray();
        newDims[dim] = 1; 
        ILSize retDimension = new ILSize(newDims);

        if (A.IsEmpty)
          return empty<fcomplex>(retDimension);

        fcomplex[] retArr = ILMemoryPool.Pool.New<fcomplex>(retDimension.NumberOfElements);

        int inc = A.Size.SequentialIndexDistance(dim);
        int dimLen = A.Size[dim];
        int maxRuns = retDimension.NumberOfElements;
        int modHelp = A.Size.NumberOfElements - 1;
        int modOut = retDimension.NumberOfElements - 1;
        int incOut = retDimension.SequentialIndexDistance(dim); 
        int numelA = A.S.NumberOfElements;
        
        fcomplex[] aArray = A.GetArrayForRead();
        if (maxRuns == 1) {
          
          fcomplex tmp = 0;
          if (computeMean)
          {
            int cnt = 0;
            for (int j = 0; j < dimLen; j++)
            {
              if (!/*HC:*/fcomplex.IsNaN(aArray[j]))
              {
                tmp += aArray[j];
                cnt++;
              }
            }
            if (cnt == 0)
              retArr[0] = fcomplex.NaN;
            else
              retArr[0] = tmp / cnt;
          }
          else
          {
            for (int j = 0; j < dimLen; j++)
            {
              if (!/*HC:*/fcomplex.IsNaN(aArray[j]))
                tmp += aArray[j];
            }
            retArr[0] = tmp;
          }
        } else {
          #region may run parallel 
          int i = 0, workItemCount = Settings.s_maxNumberThreads, workItemLength, workerCount = 1;
          if (Settings.s_maxNumberThreads > 1 && maxRuns > 1 
            && numelA / 2 >= Settings.s_minParallelElement1Count) {

            if (maxRuns >= Settings.s_maxNumberThreads
              && numelA / Settings.s_maxNumberThreads > Settings.s_minParallelElement1Count) {
              workItemLength = maxRuns / workItemCount;
            } else {
              workItemLength = maxRuns / 2;
              workItemCount = 2;
            }
            
          } else {
            workItemLength = maxRuns;
            workItemCount = 1;
          }
          Action<object> action = (data) => {
            Tuple<int, int> range = (Tuple<int, int>)data;
            int from = range.Item1, to = range.Item2;
            for (int c = from; c < to; c++) {
              int pos = (int)(((long)dimLen * c * inc) % modHelp);
              long posOut = ((long)c * incOut);
              if (posOut > modOut)
                posOut = ((posOut - 1) % modOut) + 1;
              
              fcomplex tmp = 0;
              int end = pos + dimLen * inc;
              if (computeMean)
              {
                int cnt = 0;
                for (int j = pos; j < end; j += inc)
                {
                  if (!/*HC:*/fcomplex.IsNaN(aArray[j]))
                  { 
                    tmp += aArray[j];
                    cnt++;
                  }
                }
                if (cnt == 0)
                  retArr[posOut] = fcomplex.NaN;
                else
                  retArr[posOut] = tmp / cnt;
              }
              else
              {
                 
                for (int j = pos; j < end; j += inc)
                {
                  if (!/*HC:*/fcomplex.IsNaN(aArray[j]))
                    tmp += aArray[j];
                }
                retArr[posOut] = tmp;
              }
            }
            System.Threading.Interlocked.Decrement(ref workerCount);
          };
          for (; i < workItemCount - 1; i++) {
            Interlocked.Increment(ref workerCount);

            ILThreadPool.QueueUserWorkItem(i,action, Tuple.Create(i * workItemLength, (i + 1) * workItemLength));
          }
          action(Tuple.Create(i * workItemLength, maxRuns));
          ILThreadPool.Wait4Workers(ref workerCount); 
          #endregion
        }
        return array(retArr, newDims);
      }
    }
    /// <summary>
    /// Sum elements along first non singleton dimension ignoring NaN values
    /// </summary>
    /// <param name="A">Input array</param>
    /// <param name="dim">[Optional] Index of the dimension to operate along. If omitted operates along the first non singleton dimension (i.e. != 1).</param>
    /// <returns>Sum of elements along specified of first non singleton dimension, ignoring nan values</returns>
    /// <remarks><para>The array returned will have the same size as <paramref name="A"/>, with the specified or first non singleton dimension 
    /// reduced to the length 1. It will contain the sum of all elements along that dimension after removing NaN values respectively. </para>
    /// <para>If A contains an all NaN vector along <paramref name="dim"/> , 
    /// the resulting sum will be 0 - not NaN! This corresponds to the sum of an empty vector.</para></remarks> 
    public static ILRetArray<float> nansum(ILInArray<float> A, int dim = -1)
    {
      if (dim < 0)
        dim = A.Size.WorkingDimension();
      return nansum_internal(A, dim, false);
    }
    
    /// <summary>
    /// Depending on settings, calculate nansum or nanmean
    /// </summary>
    private static ILRetArray<float> nansum_internal (ILInArray<float> A, int dim, bool computeMean) {
      using (ILScope.Enter(A)) {
        if (dim >= A.Size.NumberOfDimensions)
           return A.C;
        
        if (A.IsScalar) {
          return array<float>(new float[1] { A.GetValue(0) }, 1, 1);
        }
        
        if (A.S[dim] == 1) return A.C;

        int[] newDims = A.S.ToIntArray();
        newDims[dim] = 1; 
        ILSize retDimension = new ILSize(newDims);

        if (A.IsEmpty)
          return empty<float>(retDimension);

        float[] retArr = ILMemoryPool.Pool.New<float>(retDimension.NumberOfElements);

        int inc = A.Size.SequentialIndexDistance(dim);
        int dimLen = A.Size[dim];
        int maxRuns = retDimension.NumberOfElements;
        int modHelp = A.Size.NumberOfElements - 1;
        int modOut = retDimension.NumberOfElements - 1;
        int incOut = retDimension.SequentialIndexDistance(dim); 
        int numelA = A.S.NumberOfElements;
        
        float[] aArray = A.GetArrayForRead();
        if (maxRuns == 1) {
          
          float tmp = 0;
          if (computeMean)
          {
            int cnt = 0;
            for (int j = 0; j < dimLen; j++)
            {
              if (!/*HC:*/float.IsNaN(aArray[j]))
              {
                tmp += aArray[j];
                cnt++;
              }
            }
            if (cnt == 0)
              retArr[0] = float.NaN;
            else
              retArr[0] = tmp / cnt;
          }
          else
          {
            for (int j = 0; j < dimLen; j++)
            {
              if (!/*HC:*/float.IsNaN(aArray[j]))
                tmp += aArray[j];
            }
            retArr[0] = tmp;
          }
        } else {
          #region may run parallel 
          int i = 0, workItemCount = Settings.s_maxNumberThreads, workItemLength, workerCount = 1;
          if (Settings.s_maxNumberThreads > 1 && maxRuns > 1 
            && numelA / 2 >= Settings.s_minParallelElement1Count) {

            if (maxRuns >= Settings.s_maxNumberThreads
              && numelA / Settings.s_maxNumberThreads > Settings.s_minParallelElement1Count) {
              workItemLength = maxRuns / workItemCount;
            } else {
              workItemLength = maxRuns / 2;
              workItemCount = 2;
            }
            
          } else {
            workItemLength = maxRuns;
            workItemCount = 1;
          }
          Action<object> action = (data) => {
            Tuple<int, int> range = (Tuple<int, int>)data;
            int from = range.Item1, to = range.Item2;
            for (int c = from; c < to; c++) {
              int pos = (int)(((long)dimLen * c * inc) % modHelp);
              long posOut = ((long)c * incOut);
              if (posOut > modOut)
                posOut = ((posOut - 1) % modOut) + 1;
              
              float tmp = 0;
              int end = pos + dimLen * inc;
              if (computeMean)
              {
                int cnt = 0;
                for (int j = pos; j < end; j += inc)
                {
                  if (!/*HC:*/float.IsNaN(aArray[j]))
                  { 
                    tmp += aArray[j];
                    cnt++;
                  }
                }
                if (cnt == 0)
                  retArr[posOut] = float.NaN;
                else
                  retArr[posOut] = tmp / cnt;
              }
              else
              {
                 
                for (int j = pos; j < end; j += inc)
                {
                  if (!/*HC:*/float.IsNaN(aArray[j]))
                    tmp += aArray[j];
                }
                retArr[posOut] = tmp;
              }
            }
            System.Threading.Interlocked.Decrement(ref workerCount);
          };
          for (; i < workItemCount - 1; i++) {
            Interlocked.Increment(ref workerCount);

            ILThreadPool.QueueUserWorkItem(i,action, Tuple.Create(i * workItemLength, (i + 1) * workItemLength));
          }
          action(Tuple.Create(i * workItemLength, maxRuns));
          ILThreadPool.Wait4Workers(ref workerCount); 
          #endregion
        }
        return array(retArr, newDims);
      }
    }
    /// <summary>
    /// Sum elements along first non singleton dimension ignoring NaN values
    /// </summary>
    /// <param name="A">Input array</param>
    /// <param name="dim">[Optional] Index of the dimension to operate along. If omitted operates along the first non singleton dimension (i.e. != 1).</param>
    /// <returns>Sum of elements along specified of first non singleton dimension, ignoring nan values</returns>
    /// <remarks><para>The array returned will have the same size as <paramref name="A"/>, with the specified or first non singleton dimension 
    /// reduced to the length 1. It will contain the sum of all elements along that dimension after removing NaN values respectively. </para>
    /// <para>If A contains an all NaN vector along <paramref name="dim"/> , 
    /// the resulting sum will be 0 - not NaN! This corresponds to the sum of an empty vector.</para></remarks> 
    public static ILRetArray<complex> nansum(ILInArray<complex> A, int dim = -1)
    {
      if (dim < 0)
        dim = A.Size.WorkingDimension();
      return nansum_internal(A, dim, false);
    }
    
    /// <summary>
    /// Depending on settings, calculate nansum or nanmean
    /// </summary>
    private static ILRetArray<complex> nansum_internal (ILInArray<complex> A, int dim, bool computeMean) {
      using (ILScope.Enter(A)) {
        if (dim >= A.Size.NumberOfDimensions)
           return A.C;
        
        if (A.IsScalar) {
          return array<complex>(new complex[1] { A.GetValue(0) }, 1, 1);
        }
        
        if (A.S[dim] == 1) return A.C;

        int[] newDims = A.S.ToIntArray();
        newDims[dim] = 1; 
        ILSize retDimension = new ILSize(newDims);

        if (A.IsEmpty)
          return empty<complex>(retDimension);

        complex[] retArr = ILMemoryPool.Pool.New<complex>(retDimension.NumberOfElements);

        int inc = A.Size.SequentialIndexDistance(dim);
        int dimLen = A.Size[dim];
        int maxRuns = retDimension.NumberOfElements;
        int modHelp = A.Size.NumberOfElements - 1;
        int modOut = retDimension.NumberOfElements - 1;
        int incOut = retDimension.SequentialIndexDistance(dim); 
        int numelA = A.S.NumberOfElements;
        
        complex[] aArray = A.GetArrayForRead();
        if (maxRuns == 1) {
          
          complex tmp = 0;
          if (computeMean)
          {
            int cnt = 0;
            for (int j = 0; j < dimLen; j++)
            {
              if (!/*HC:*/complex.IsNaN(aArray[j]))
              {
                tmp += aArray[j];
                cnt++;
              }
            }
            if (cnt == 0)
              retArr[0] = complex.NaN;
            else
              retArr[0] = tmp / cnt;
          }
          else
          {
            for (int j = 0; j < dimLen; j++)
            {
              if (!/*HC:*/complex.IsNaN(aArray[j]))
                tmp += aArray[j];
            }
            retArr[0] = tmp;
          }
        } else {
          #region may run parallel 
          int i = 0, workItemCount = Settings.s_maxNumberThreads, workItemLength, workerCount = 1;
          if (Settings.s_maxNumberThreads > 1 && maxRuns > 1 
            && numelA / 2 >= Settings.s_minParallelElement1Count) {

            if (maxRuns >= Settings.s_maxNumberThreads
              && numelA / Settings.s_maxNumberThreads > Settings.s_minParallelElement1Count) {
              workItemLength = maxRuns / workItemCount;
            } else {
              workItemLength = maxRuns / 2;
              workItemCount = 2;
            }
            
          } else {
            workItemLength = maxRuns;
            workItemCount = 1;
          }
          Action<object> action = (data) => {
            Tuple<int, int> range = (Tuple<int, int>)data;
            int from = range.Item1, to = range.Item2;
            for (int c = from; c < to; c++) {
              int pos = (int)(((long)dimLen * c * inc) % modHelp);
              long posOut = ((long)c * incOut);
              if (posOut > modOut)
                posOut = ((posOut - 1) % modOut) + 1;
              
              complex tmp = 0;
              int end = pos + dimLen * inc;
              if (computeMean)
              {
                int cnt = 0;
                for (int j = pos; j < end; j += inc)
                {
                  if (!/*HC:*/complex.IsNaN(aArray[j]))
                  { 
                    tmp += aArray[j];
                    cnt++;
                  }
                }
                if (cnt == 0)
                  retArr[posOut] = complex.NaN;
                else
                  retArr[posOut] = tmp / cnt;
              }
              else
              {
                 
                for (int j = pos; j < end; j += inc)
                {
                  if (!/*HC:*/complex.IsNaN(aArray[j]))
                    tmp += aArray[j];
                }
                retArr[posOut] = tmp;
              }
            }
            System.Threading.Interlocked.Decrement(ref workerCount);
          };
          for (; i < workItemCount - 1; i++) {
            Interlocked.Increment(ref workerCount);

            ILThreadPool.QueueUserWorkItem(i,action, Tuple.Create(i * workItemLength, (i + 1) * workItemLength));
          }
          action(Tuple.Create(i * workItemLength, maxRuns));
          ILThreadPool.Wait4Workers(ref workerCount); 
          #endregion
        }
        return array(retArr, newDims);
      }
    }

#endregion HYCALPER AUTO GENERATED CODE
}
}