utils.cc File Reference

#include "utils.h"
#include "Uniform.h"
#include "tmatrix.h"
#include <string>

Functions
double	my_mean (double *data, unsigned int size)
double	my_mean_no_nan (double *data, unsigned int size)
double	my_variance_with_fixed_mean (double *data, unsigned int size, double mean)
double	my_variance_with_fixed_mean_no_nan (double *data, unsigned int size, double mean)
double	my_covariance_no_nan (double *data1, double *data2, unsigned int size1, unsigned int size2)
bool	setSpatialMatrix (string param, string numColCondition, TMatrix *inMat, TMatrix *outMat, unsigned int nVal, unsigned int patchNbr, bool doRandomize)
unsigned long	nChooseK (int n, int k)
TMatrix	nChooseKVec (int n, int k)

Detailed Description

Id

utils.cc,v 1.7 2018/09/28 16:14:00 fred Exp

Nemo2

Copyright (C) 2013 Frederic Guillaume frede.nosp@m.ric..nosp@m.guill.nosp@m.aume.nosp@m.@ieu..nosp@m.uzh..nosp@m.ch

This file is part of Nemo

Nemo is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version.

Nemo 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 this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA

created on

Date

02.05.2013

Author

fred

Function Documentation

my_covariance_no_nan()

double my_covariance_no_nan	(	double *	data1,
		double *	data2,
		unsigned int	size1,
		unsigned int	size2
	)

{
  assert(size1 == size2);
 
  double var = 0, cnt = 0;
 
  for (unsigned int i = 0; i < size1; ++i) {
 
    if( !isnan(data1[i]) && !isnan(data2[i])) {
 
      for (unsigned int j = i+1; j < size1; ++j) {
 
        if( !isnan(data1[j]) && !isnan(data2[j])) {
          var += ( data1[i] - data1[j])*(data2[i] - data2[j]);
          cnt++;
        }
 
      }
    }
  }
  cnt *= cnt;
  return var/cnt;
}

Referenced by TTQuantiSH::setStats().

my_mean()

double my_mean	(	double *	data,
		unsigned int	size
	)

{
  double mean = 0;
  for (unsigned int i = 0; i < size; ++i) {
    mean += data[i];
  }
  return mean/size;
}

Referenced by LCE_Selection_base::addPhenotypicSD(), and TTQuantiSH::setStats().

my_mean_no_nan()

double my_mean_no_nan	(	double *	data,
		unsigned int	size
	)

{
  double mean = 0, cnt = 0;
  for (unsigned int i = 0; i < size; ++i) {
    if( !isnan(data[i]) ) { //exclude NaN values (happens for empty patches)
      mean += data[i];
      cnt++;
    }
  }
  return mean/cnt;
}

Referenced by TTQuantiSH::setStats().

my_variance_with_fixed_mean()

double my_variance_with_fixed_mean	(	double *	data,
		unsigned int	size,
		double	mean
	)

{
  double var = 0;
  for (unsigned int i = 0; i < size; ++i) {
    var += pow( data[i] - mean, 2 );
  }
  return var/size;
}

Referenced by LCE_Selection_base::addPhenotypicSD(), TTQuantiSH::getVaNoDominance(), TTQuantiSH::getVaWithDominance(), and TTQuantiSH::setStats().

my_variance_with_fixed_mean_no_nan()

double my_variance_with_fixed_mean_no_nan	(	double *	data,
		unsigned int	size,
		double	mean
	)

{
  double var = 0, cnt = 0;
  for (unsigned int i = 0; i < size; ++i) {
    if( !isnan(data[i]) ) {
      var += pow( data[i] - mean, 2 );
      cnt++;
    }
  }
  return var/cnt;
}

Referenced by TTQuantiSH::setStats().

nChooseK()

unsigned long nChooseK	(	int	n,
		int	k
	)

{
  if (!(n >= k && k >= 0)) {
    fatal("nChooseK() requires (n >= k && k >= 0)\n");
  }
 
  if ((k == 0) || (n == k)) {
    return 1;
  }
 
  unsigned long result = n;
  int i = 2;
  n = n-1; k = k-1;
  while (k != 0){
    result = result * n / i;
    i = i+1;
    k = k-1;
    n = n-1;
  }
  return result;
}

References fatal().

Referenced by nChooseKVec(), and TProtoQuanti::setEpistasisParameters().

nChooseKVec()

TMatrix nChooseKVec	(	int	n,
		int	k
	)

{
  vector<double> result(k,0);
 
  unsigned long ncombs = nChooseK(n, k);
 
  TMatrix results(ncombs , k);
 
  int i, K, N, counter;
  double c;
 
  for (int x = 0 ; x < ncombs ; ++x) {
 
    i = x; K = k; N = n;
    c = ncombs * K / N;
    counter = 0;
 
    for (int element = 0; element < n; ++element){
      if (i < c) {
        //take this element, and K-1 from the remaining
        result[counter] = element;
        ++counter;
        K = K-1;
        if (K == 0) {
          break;
        }
        //have c == nChooseK(N-1,K), want nChooseK(N-2,K-1)
        c = c * K / (N-1);
      }
      else {
        //skip this element, and take all from the remaining
        i = i-c;
        //have c == nChooseK(N-1,K-1), want nChooseK(N-2,K-1)
        c = c * (N-K) / (N-1);
      }
      N = N-1;
    }
//    cout << "{ ";
//    for (int x = 0 ; x < k-1 ; x++)
//      cout << result[x] << " ";
//    cout << result[k-1] << " }" << endl;
 
    counter = 0;
    results. set_row(x, result);
  }
  return results;
}

References nChooseK().

Referenced by TTNOhtaStats::FHwrite(), TTQOhtaStats::FHwrite(), and TProtoQuanti::setEpistasisParameters().

setSpatialMatrix()

bool setSpatialMatrix	(	string	param,
		string	numColCondition,
		TMatrix *	inMat,
		TMatrix *	outMat,
		unsigned int	nVal,
		unsigned int	patchNbr,
		bool	doRandomize
	)

{
  unsigned int ncol = inMat->getNbCols(); //nbr of traits/loci = nbr of col
  unsigned int npat = inMat->getNbRows(); //nbr of patches, may be <= patchNbr
  
  //we have two possible configurations:
  // 1. ncol == 1; same value for all 'traits' in a patch but varies among patches, with possible repetition of a pattern
  // 2. ncol == no traits && npat <= no patches; trait values change in each patch following a pattern (same if npat == 1)
  if(npat > patchNbr) {
    error("The number of rows in \"%s\" is greater than the number of patches, must be at least equal to it.", param.c_str());
    return false;
  }  
  if(ncol != 1 && ncol != nVal) {
    error("The number of columns in \"%s\" not properly set.\n", param.c_str());
    error("It is expected to be equal to 1 or equal to %s (%i).\n", numColCondition.c_str(), nVal);
    return false;
  }
  
  outMat->reset(patchNbr, nVal);
  
  if(doRandomize) {
    
    for(unsigned int i = 0; i < patchNbr; ++i)
      for(unsigned int j = 0; j < nVal; j++)
        outMat->set(i, j, inMat->get( RAND::Uniform(npat), j));
    
  } else {
    
    for(unsigned int i = 0; i < patchNbr; ++i) {
      
      if(npat < patchNbr) {//repetition of a pattern
        
        if(ncol == 1)
          for(unsigned int j = 0; j < nVal; j++) 
            outMat->set(i, j, inMat->get(i % npat, 0)) ;
        else
          for(unsigned int j = 0; j < nVal; j++)
            outMat->set(i, j, inMat->get(i % npat, j));
        
      } else {//different values for each Patch
        
        if(ncol == 1)
          for(unsigned int j = 0; j < nVal; j++) 
            outMat->set(i, j, inMat->get(i, 0));
        else
          for(unsigned int j = 0; j < nVal; j++)
            outMat->set(i, j, inMat->get(i, j));
      }
    }
  }
  
  
 
  return true;
}

References error(), TMatrix::get(), TMatrix::getNbCols(), TMatrix::getNbRows(), TMatrix::reset(), TMatrix::set(), and RAND::Uniform().

Referenced by LCE_PhenotypeExpression::set_env_cue(), LCE_Selection_base::set_local_optima(), LCE_PhenotypeExpression::setParameters(), LCE_QuantiInit::setParameters(), LCE_NtrlInit::setParameters(), LCE_Breed_Wolbachia::setParameters(), and LCE_Selection_base::setSelectionMatrix().