NEURON interface to GAUL (Neymotin and Lytton)

Accession:102464
This interface allows the use of genetic algorithms for optimization and search in high-dimensional spaces from within the NEURON environment. It includes converted .c,.h files from GAUL wrapped in proper MOD file syntax as well as MOD code interfacing to the library. It also comes with hoc utilitiy functions to make it easier to use the GA.
Tool Information (Click on a link to find other Tools with that property)
Tool Type: Control Simulations;
Simulation Environment: NEURON;
\
neuron_gaul_2
gaul
readme.txt
compatibility.mod
ga_bitstring.mod
ga_chromo.mod
ga_climbing.mod
ga_compare.mod
ga_core.mod
ga_crossover.mod
ga_de.mod
ga_deterministiccrowding.mod
ga_gradient.mod
ga_hoc.mod
ga_intrinsics.mod
ga_io.mod
ga_mutate.mod
ga_optim.mod
ga_qsort.mod
ga_randomsearch.mod
ga_rank.mod
ga_replace.mod
ga_sa.mod
ga_seed.mod
ga_select.mod
ga_similarity.mod
ga_simplex.mod
ga_stats.mod
ga_systematicsearch.mod
ga_tabu.mod
ga_utility.mod
linkedlist.mod
log_util.mod
memory_chunks.mod
memory_util.mod
nn_util.mod
random_util.mod
avltree.mod
table_util.mod
timer_util.mod
vecst.mod
mosinit.hoc
ga_utils.hoc
init.hoc
declist.hoc
setup.hoc
decvec.hoc
ga_test.hoc
gaul.h
xtmp
                            
:$Id: ga_stats.mod,v 1.1 2006/12/22 16:49:52 samn Exp $
NEURON {
  SUFFIX nothing
}

VERBATIM

/**********************************************************************
  ga_stats.c
 **********************************************************************

  ga_stats - Statistics routines.
  Copyright   2000-2002, Stewart Adcock <stewart@linux-domain.com>
  All rights reserved.

  The latest version of this program should be available at:
  http://gaul.sourceforge.net/

  This program 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.  Alternatively, if your project
  is incompatible with the GPL, I will probably agree to requests
  for permission to use the terms of any other license.

  This program is distributed in the hope that it will be useful, but
  WITHOUT ANY WARRANTY WHATSOEVER.

  A full copy of the GNU General Public License should be in the file
  "COPYING" provided with this distribution; if not, see:
  http://www.gnu.org/

 **********************************************************************

  Synopsis:     Convenience statistics functions.

  To do:	On-line and off-line performance summaries.

 **********************************************************************/

#include "gaul.h"

/**********************************************************************
  ga_fitness_mean()
  synopsis:     Determine mean of the fitness scores.
  parameters:	population *pop		The population to evaluate.
  		double *mean		Returns the mean fitness.
  return:	TRUE on success.
  last updated: 07 Jul 2004
 **********************************************************************/

boolean ga_fitness_mean( population *pop, double *mean )
  {
  int           i;           /* Loop over all entities. */
  double        sum=0.0;     /* Sum of fitnesses. */

  if (!pop) die("Null pointer to population structure passed.");
  if (pop->size < 1) die("Pointer to empty population structure passed.");
  if (!mean) die("Null pointer to double passed.");

  for (i=0; i<pop->size; i++)
    {
    sum += pop->entity_iarray[i]->fitness;
    }

  *mean = sum / pop->size;

  return TRUE;
  }


/**********************************************************************
  ga_fitness_mean_stddev()
  synopsis:     Determine mean and standard deviation of the fitness
                scores.
  parameters:	population *pop		The population to evaluate.
  		double *mean		Returns the mean fitness.
		double *stddev		Returns the standard deviation of the fitnesses.
  return:	TRUE on success.
  last updated: 07 Jul 2004
 **********************************************************************/

boolean ga_fitness_mean_stddev( population *pop,
                             double *mean, double *stddev )
  {
  int           i;                      /* Loop over all entities. */
  double        sum=0.0, sumsq=0.0;     /* Sum and sum squared. */
  double	deviation;		/* Distance to mean. */

  if (!pop) die("Null pointer to population structure passed.");
  if (pop->size < 1) die("Pointer to empty population structure passed.");
  if (!stddev || !mean) die("Null pointer to double passed.");

  for (i=0; i<pop->size; i++)
    {
    sum += pop->entity_iarray[i]->fitness;
    }

  *mean = sum / pop->size;

  for (i=0; i<pop->size; i++)
    {
    deviation = pop->entity_iarray[i]->fitness - *mean;
    sumsq += deviation*deviation;
    }

  *stddev = sqrt(sumsq/pop->size);

  return TRUE;
  }


/**********************************************************************
  ga_fitness_stats()
  synopsis:     Determine some stats about the fitness scores.
  parameters:	population *pop		The population to evaluate.
  		double *mean		Returns the average fitness.
  		double *median		Returns the median fitness.
		double *variance	Returns the variance of the fitnesses.
		double *stddev		Returns the standard deviation of the fitnesses.
		double *kurtosis	Returns the kurtosis of the fitnesses.
		double *skew		Returns the skew of the fitnesses.
  return:	TRUE on success.
  last updated: 17 Jul 2004
 **********************************************************************/

boolean ga_fitness_stats( population *pop,
                          double *max, double *min,
                          double *mean, double *median,
                          double *variance, double *stddev,
                          double *kurtosis, double *skew )
  {
  int           i;                      /* Loop over all entities. */
  double	sum2=0.0,sum3=0.0,sum4=0.0;	/* Distribution moments (x pop->size). */
  double	tmp=0.0;		/* Used to save some lookups. */

  if (!pop) die("Null pointer to population structure passed.");
  if (pop->size < 1) die("Pointer to empty population structure passed.");
  if (!max || !min || !mean || !variance || !stddev || !kurtosis || !skew)
    die("Null pointer to double passed.");

  *min = pop->entity_iarray[0]->fitness;
  *max = pop->entity_iarray[pop->size-1]->fitness;
  *median = *min + (*max - *min)/2;

  for (i=0; i<pop->size; i++)
    {
    tmp += pop->entity_iarray[i]->fitness;
    }

  *mean = tmp / pop->size;

  for (i=0; i<pop->size; i++)
    {
    tmp = pop->entity_iarray[i]->fitness - *mean;
    sum2 += tmp*tmp;
    sum3 += tmp*tmp*tmp;	/* I hope my compiler optimises this... */
    sum4 += tmp*tmp*tmp*tmp;
    }

  *variance = sum2/pop->size;
  *skew = (sum3/pop->size)/pow(*variance,3.0/2.0);
  *kurtosis = (sum4/pop->size)/(*variance * *variance);
  *stddev = sqrt(*variance);

  return TRUE;
  }

ENDVERBATIM