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

fitting.c

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

fitting - Test/example program for GAUL.

Copyright ©2002-2004, 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: Test/example program for GAUL.

This program aims to fit a function of the form

y = Ax exp{Bx+C} + D

through an input dataset.

Last Updated: 19 Mar 2004 SAA Custom mutation function since existing function produced mutations that were far too significant.

21 Nov 2002 SAA New seeding function.

17 Nov 2002 SAA Initial version.

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

#include "rlc_solver.h"

#include "double_array.h"

#include "gaul.h"

/*

* Datastructure used to demonstrate attachment of data to specific

* populations.

* It is used to store the training data.

*/

typedef struct

{

int size;

double *x;

double *y;

} fitting_data_t;

void debug_message(const char * message)

{

fprintf(stderr, message);

}

#define E_GIVEN 10.0

#define ARGLEN 3

const double ARGSCALE[ARGLEN] = {1.0, 0.01, 0.01};

/**

* params_c: R, L, C, untranlated

* params_d: E, R, L, C in double

*

*/

int chromo_translator(const char * params_c, double * params_d)

{

int i;

// R,L,C

for(i = 0; i < ARGLEN; i++)

params_d[i+1]= ((unsigned short)(params_c[i]) + 1) * ARGSCALE[i];

params_d[0] = E_GIVEN;

return 1; // success

}

int sort_double_func(const void * d1, const void * d2)

{

return (*((double*)d1)) > (*((double*)d2));

}

/**

* params_given: E

* params_genome: R,L,C

* double x: time

* init_value: U_0, U'_0

* return_value: U_t, U'_t

*/

double target_function(double t, double * params)

{

int status;

double E,C,L,R;

double init_value[2];

double return_value[2];

E = params[0];

R = params[1];

L = params[2];

C = params[3];

init_value[0] = 0.0;

init_value[1] = E/(R*C);

status = rlc_solver(t, 0.0, init_value, return_value, params);

return return_value[0];

}

boolean fitting_score(population *pop, entity *entity)

{

int i; /* Loop variable over training points. */

double score=0.0; /* Mean of squared deviations. */

char * params;

double params_d[4]; /* Fitting parameters. */

fitting_data_t *data; /* Training data. */

double E,R,L,C;

entity->fitness = 0;

data = (fitting_data_t *)pop->data;

params = (char *)entity->chromosome[0];

chromo_translator(params, params_d);

E = params_d[0];

R = params_d[1];

L = params_d[2];

C = params_d[3];

for (i=0; i<data->size; i++)

{

if(score > 10000000)

{

score = 10000000;

break;

}

score += SQU(data->y[i]-(target_function(data->x[i], params_d)));

}

entity->fitness = -sqrt(score / data->size);

return TRUE;

}

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

fitting_generation_callback()

synopsis: Generation callback

parameters:

return:

updated: 17 Nov 2002

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

boolean fitting_generation_callback(int generation, population *pop)

{

double params_d[4];

chromo_translator((char*)(pop->entity_iarray[0]->chromosome[0]), params_d);

fprintf(stdout, "%d: E=%8.2lf\tR=%8.2lf\tL=%4.2lf\tC=%4.2lf\t\tFitness: %lf\n",

generation,

params_d[0],

params_d[1],

params_d[2],

params_d[3],

pop->entity_iarray[0]->fitness );

return TRUE;

}

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

get_data()

synopsis: Read training data from standard input.

parameters:

return:

updated: 17 Nov 2002

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

void get_data(FILE * fIn, fitting_data_t * data)

{

int n;

//int line_count = 0;

DoubleArray da_x;

DoubleArray da_y;

double x,y;

DoubleArray_Initialize(&da_x);

DoubleArray_Initialize(&da_y);

while(1)

{

n = fscanf(fIn, "%lf %lf\n", &x, &y);

if(n < 2)

break;

DoubleArray_Insert(&da_x, x);

DoubleArray_Insert(&da_y, y);

}

assert(da_x.length == da_y.length);

//dp.size = da_x.length;

data->size = da_x.length;

data->x = (double*)malloc(sizeof(double)*data->size);

data->y = (double*)malloc(sizeof(double)*data->size);

memcpy(data->x, da_x.data, data->size * sizeof(double));

memcpy(data->y, da_y.data, data->size * sizeof(double));

}

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

main()

synopsis: Main function.

parameters:

return:

updated: 17 Nov 2002

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

int main(int argc, char **argv)

{

population *pop; /* Population of solutions. */

fitting_data_t data; /* Training data. */

FILE * fData;

random_seed(time(NULL));

double params_d[4];

double x;

// fitting

if(argc >= 3 && strcmp(argv[1], "fit") == 0)

{

pop = ga_genesis_char(

100, /* const int population_size */

1, /* const int num_chromo */

3, /* const int len_chromo */

fitting_generation_callback, /* GAgeneration_hook generation_hook */

NULL, /* GAiteration_hook iteration_hook */

NULL, /* GAdata_destructor data_destructor */

NULL, /* GAdata_ref_incrementor data_ref_incrementor */

fitting_score, /* GAevaluate evaluate */

//fitting_seed, /* GAseed seed */

ga_seed_char_random,

NULL, /* GAadapt adapt */

//ga_select_one_linearrank,

ga_select_one_random, /* GAselect_one select_one */

ga_select_two_random, /* GAselect_two select_two */

ga_mutate_char_singlepoint_randomize,/* GAmutate mutate */

ga_crossover_char_allele_mixing,/* GAcrossover crossover */

NULL, /* GAreplace replace */

NULL /* vpointer User data */

);

ga_population_set_parameters(

pop, /* population *pop */

GA_SCHEME_DARWIN, /* const ga_scheme_type scheme */

GA_ELITISM_PARENTS_DIE, /* const ga_elitism_type elitism */

0.8, /* double crossover */

0.8, /* double mutation */

0.0 /* double migration */

);

fData = fopen("data", "r");

debug_message("Begin Get Data\n");

get_data(fData, &data);

pop->data = &data;

debug_message("Done Get Data\n");

debug_message("Begin Evolution\n");

ga_evolution(

pop, /* population *pop */

200 /* const int max_generations */

);

debug_message("Done Evolution\n");

ga_extinction(pop);

}

// plot

else if(argc >= 5 && strcmp(argv[1], "plot")==0)

{

params_d[0] = E_GIVEN;

params_d[1] = atof(argv[2]);

params_d[2] = atof(argv[3]);

params_d[3] = atof(argv[4]);

for(x = 0; x < 10; x += 0.01)

{

fprintf(stdout, "%lf %lf\n", x, target_function(x, params_d));

}

}

else

{

fprintf(stdout, "usage: %s [plot/fit] [params]\n", argv[0]);

}

}