diff options
Diffstat (limited to 'genetic.c')
| -rwxr-xr-x | genetic.c | 218 |
1 files changed, 218 insertions, 0 deletions
diff --git a/genetic.c b/genetic.c new file mode 100755 index 0000000..5c908f2 --- /dev/null +++ b/genetic.c @@ -0,0 +1,218 @@ +#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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