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#include "m_pd.h"
#include <math.h>
#include <stdio.h>
#include <stdlib.h>
#define TRUE 1
#define FALSE 0
#define MAX_ARRAY_SIZE 256
//typedef short boolean;
static t_class *genetic_class;
typedef struct _individual {
boolean genes[MAX_ARRAY_SIZE];
float fitness;
t_int length;
} t_individual;
typedef struct _genetic {
t_object x_obj;
float cumulativeprobability;
t_int individuallength;
t_individual population[MAX_ARRAY_SIZE];
t_int populationsize;
t_individual targetindividual;
t_atom out[MAX_ARRAY_SIZE];
t_int chunksize;
t_outlet *outlist;
} t_genetic;
void randomizeIndividuals(t_genetic *x) {
int i,k;
for (k=0;k<x->populationsize;k++) {
for (i=0;i<x->individuallength;i++) {
x->population[k].genes[i] = ((t_float) rand())/RAND_MAX*2;
}
x->population[k].length = x->individuallength;
}
// x->population[0].genes[0] = 1;
// x->population[0].genes[1] = 0;
}
void printTarget(t_genetic *x) {
unsigned int out[MAX_ARRAY_SIZE];
int i;
for (i=0;i< x->targetindividual.length/x->chunksize;i++) {
fast_b2short(&out[i],&x->targetindividual.genes[i*x->chunksize],x->chunksize);
SETFLOAT(&x->out[i],out[i]);
}
outlet_list(x->outlist,&s_list, i, &x->out[0]);
}
void randomTargetIndividual(t_genetic *x) {
int i;
for (i=0;i<x->individuallength;i++) {
x->targetindividual.genes[i] = ((t_float) rand())/RAND_MAX*2;
}
x->targetindividual.length = x->individuallength;
//printTarget(&x->targetindividual,x);
}
/*float evaluateFitness(t_genetic *x,int index) {
int fitness = 0;
int i;
// post("Fitness eval");
for (i=0;i<x->individuallength;i++) {
//if (x->targetindividual.genes[i] == x->population[index].genes[i])
//if (x->population[index].genes[i] == TRUE)
fitness += x->population[0].genes[0];
//fitness++;
}
return fitness/x->individuallength;
//return 1;
}
*/
void startReproduction(t_genetic *x) {
int selectedIndividuals[MAX_ARRAY_SIZE];
int count = 0;
int i;
float r;
t_individual childs[MAX_ARRAY_SIZE];
int childindex = 0;
for (count =0; count < x->populationsize/4; count++) {
float sum = 0;
i = 0;
r = ((t_float) rand())/RAND_MAX * x->cumulativeprobability;
while (r > sum) {
sum += x->population[i].fitness;
i++;
}
selectedIndividuals[count] = i;
}
post("parents: %d",count);
i=0;
while (i<count) {
int k = 0;
for (k=0;k<8;k++) {
int l;
r = ((t_float) rand())/RAND_MAX * x->individuallength;
for (l=0;l<x->individuallength;l++) {
if (l<r) childs[childindex].genes[l] = x->population[selectedIndividuals[i]].genes[l];
else childs[childindex].genes[l] = x->population[selectedIndividuals[i+1]].genes[l];
}
// mutation
r = ((t_float) rand())/RAND_MAX * x->individuallength;
childs[childindex].genes[(int)r] = 1-childs[childindex].genes[(int)r];
childindex++;
}
i+=2;
}
for (i=0;i<childindex;i++) {
x->population[i] = childs[i];
}
post("New childs: %d",childindex);
}
int evaluatePopulation(t_genetic *x) {
int i,k;
int f;
int highestfitnessindex = 0;
float highestfitness = 0;
float mean;
x->cumulativeprobability = 0;
for (k=0; k < x->populationsize; k++) {
float fitness = 0;
for (i=0;i<x->individuallength;i++) {
//x->population[k].genes[i] = 0;
if (x->targetindividual.genes[i] == x->population[k].genes[i])
fitness++;
// fitness += x->population[k].genes[i];
}
x->population[k].fitness = fitness/x->individuallength;
x->population[k].fitness = (fitness/x->individuallength)*(fitness/x->individuallength);
if (x->population[k].fitness > highestfitness) {
// post("fitness of %d: %f",k,fitness);
highestfitness = x->population[k].fitness;
highestfitnessindex = k;
}
x->cumulativeprobability+=x->population[k].fitness;
}
//recalculate
mean = x->cumulativeprobability/x->populationsize;
x->cumulativeprobability = 0;
for (k=0;k < x->populationsize; k++) {
if (x->population[k].fitness < mean)
x->population[k].fitness = 0;
else x->cumulativeprobability += x->population[k].fitness;
}
post("Highest fitness: %f",highestfitness);
return highestfitnessindex;
}
void genetic_setTarget(t_genetic *x,t_symbol *s, int argc, t_atom *argv) {
int i;
post("target called: %d",argc);
for (i=0;i< x->individuallength/x->chunksize;i++) {
if (i >= argc) break;
fast_d2bl(atom_getint(&argv[i]),&x->targetindividual.genes[i*x->chunksize],x->chunksize);
}
printTarget(x);
}
void genetic_randomize(t_genetic *x) {
post("Randomize called");
randomizeIndividuals(x);
}
void genetic_bang(t_genetic *x)
{
int selected = 1;
unsigned int out[MAX_ARRAY_SIZE];
int i;
selected = evaluatePopulation(x);
//post("Selected individual: %d",selected);
for (i=0;i< x->individuallength/x->chunksize;i++) {
//out[i] = ((t_float) rand())/RAND_MAX*2;
fast_b2short(&out[i],&x->population[selected].genes[i*x->chunksize],x->chunksize);
//fast_b2short8(&out[i],&x->population[selected].genes[i*8]);
SETFLOAT(&x->out[i],out[i]);
}
// fast_b2d(&out,&x->population[0].genes[0]);
// fast_b2short(&out,&x->population[0].genes[0]);
// fast_b2d(&out,&test[0]);
// out = 100;
// SETFLOAT(&x->out[1],out);
outlet_list(x->outlist,&s_list, i, &x->out[0]);
startReproduction(x);
}
/*
* take argument rule, size, and offset
*/
void *genetic_new(t_float popsize,t_float indsize, t_float chunksize)
{
t_genetic *x = (t_genetic *)pd_new(genetic_class);
//inlet_new(&x->x_obj, &x->x_obj.ob_pd,gensym("list"), gensym("randomize"));
if ((popsize > 0) && (popsize <= 256))
x->populationsize = popsize;
else x->populationsize = 256;
if ((indsize > 0) && (indsize <= 256))
x->individuallength = indsize;
else x->individuallength = 256;
if ((chunksize <= 16) && (chunksize > 0))
x->chunksize = (int) chunksize;
else x->chunksize = 8;
randomizeIndividuals(x);
randomTargetIndividual(x);
inlet_new(&x->x_obj, &x->x_obj.ob_pd,gensym("list"), gensym("target"));
x->outlist = outlet_new(&x->x_obj,&s_list);
return (void *)x;
}
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