/**
* @brief Initialise an autocoder
* @param autocoder Autocoder object
* @param no_of_inputs The number of inputs
* @param no_of_hiddens The number of hidden (encoder) units
* @param random_seed Random number generator seed
* @return zero on success
*/
int autocoder_init(ac * autocoder,
int no_of_inputs,
int no_of_hiddens,
unsigned int random_seed)
{
autocoder->NoOfInputs = no_of_inputs;
autocoder->NoOfHiddens = no_of_hiddens;
autocoder->inputs =
(float*)malloc(no_of_inputs*sizeof(float));
if (!autocoder->inputs) return -1;
autocoder->hiddens =
(float*)malloc(no_of_hiddens*sizeof(float));
if (!autocoder->hiddens) return -2;
autocoder->bias =
(float*)malloc(no_of_hiddens*sizeof(float));
if (!autocoder->bias) return -3;
autocoder->weights =
(float*)malloc(no_of_hiddens*no_of_inputs*sizeof(float));
if (!autocoder->weights) return -4;
autocoder->lastWeightChange =
(float*)malloc(no_of_hiddens*no_of_inputs*sizeof(float));
if (!autocoder->lastWeightChange) return -5;
autocoder->outputs =
(float*)malloc(no_of_inputs*sizeof(float));
if (!autocoder->outputs) return -6;
autocoder->bperr =
(float*)malloc(no_of_hiddens*sizeof(float));
if (!autocoder->bperr) return -7;
autocoder->lastBiasChange =
(float*)malloc(no_of_hiddens*sizeof(float));
if (!autocoder->lastBiasChange) return -8;
memset((void*)autocoder->inputs,'\0',no_of_inputs*sizeof(float));
memset((void*)autocoder->outputs,'\0',no_of_inputs*sizeof(float));
memset((void*)autocoder->hiddens,'\0',
no_of_hiddens*sizeof(float));
memset((void*)autocoder->lastWeightChange,'\0',
no_of_hiddens*no_of_inputs*sizeof(float));
memset((void*)autocoder->bperr,'\0',
autocoder->NoOfHiddens*sizeof(float));
memset((void*)autocoder->lastBiasChange,'\0',
autocoder->NoOfHiddens*sizeof(float));
autocoder->BPerror = AUTOCODER_UNKNOWN;
autocoder->BPerrorAverage = AUTOCODER_UNKNOWN;
autocoder->learningRate = 0.2f;
autocoder->noise = 0;
autocoder->random_seed = random_seed;
autocoder->itterations = 0;
autocoder->DropoutPercent = 0.01f;
/* initial small random values */
for (int h = 0; h < no_of_hiddens; h++) {
autocoder->bias[h] =
(0.2f*(rand_num(&autocoder->random_seed)%10000/10000.0f))-0.1f;
for (int i = 0; i < no_of_inputs; i++) {
autocoder->weights[h*no_of_inputs + i] =
(0.2f*(rand_num(&autocoder->random_seed)%10000/10000.0f))-0.1f;
}
}
return 0;
}
/**
* @brief Encodes the inputs to a given array
* @param autocoder Autocoder object
* @param encoded Array to store the encoded values
* @param use_dropouts If non-zero then allow dropouts in the returned results
*/
void autocoder_encode(ac * autocoder, float * encoded, unsigned char use_dropouts)
{
for (int h = 0; h < autocoder->NoOfHiddens; h++) {
if (use_dropouts != 0) {
if (rand_num(&autocoder->random_seed)%10000 <
autocoder->DropoutPercent*100) {
autocoder->hiddens[h] = (int)AUTOCODER_DROPPED_OUT;
continue;
}
}
/* weighted sum of inputs */
float adder = autocoder->bias[h];
for (int i = 0; i < autocoder->NoOfInputs; i++) {
adder +=
autocoder->weights[h*autocoder->NoOfInputs + i] *
autocoder->inputs[i];
}
/* add some random noise */
if (autocoder->noise > 0) {
adder = ((1.0f - autocoder->noise) * adder) +
(autocoder->noise * ((rand_num(&autocoder->random_seed)%10000)/10000.0f));
}
/* activation function */
encoded[h] = 1.0f / (1.0f + exp(-adder));
}
}
void generate_colors()
{
srand(time(NULL));
int i;
for(i=0; i<MAX*5; i++)
{
color[i].r = rand_num();
color[i].g = rand_num();
color[i].b = rand_num();
}
}
static unsigned int search_mem(void)
{
record_t key, *found;
record_t *src, *copy;
unsigned int chunk;
size_t copy_size = chunk_size;
unsigned int i;
unsigned int state = 0;
for (i = 0; threads_go == 1; i++) {
chunk = rand_num(chunks, &state);
src = mem[chunk];
/*
* If we're doing random sizes, we need a non-zero
* multiple of record size.
*/
if (random_size)
copy_size = (rand_num(chunk_size / record_size, &state)
+ 1) * record_size;
copy = alloc_mem(copy_size);
if (touch_pages) {
touch_mem((char *)copy, copy_size);
} else {
if (no_lib_memcpy)
my_memcpy(copy, src, copy_size);
else
memcpy(copy, src, copy_size);
key = rand_num(copy_size / record_size, &state);
if (verbose > 2)
printf("Search key %zu, copy size %zu\n", key,
copy_size);
if (linear)
found = linear_search(key, copy, copy_size);
else
found =
bsearch(&key, copy, copy_size / record_size,
record_size, compare);
/* Below check is mainly for memory corruption or other bug */
if (found == NULL) {
fprintf(stderr, "Couldn't find key %zd\n", key);
exit(1);
}
} /* end if ! touch_pages */
free_mem(copy, copy_size);
}
return (i);
}
void bubbles()
{
int r, c, b;
int size;
int centerx, centery;
int outradius, inradius, midradius;
int bubblesx, bubblesy; /* number of bubbles in a row or column */
int numbubbles;
clear_invert_map();
/*
* outer is the main bubble
* inner is the black dot in the bubble
* what's the middle??
*/
size = (map.sec_width > map.sec_height ? map.sec_width : map.sec_height);
outradius = rand_num(size/3, size);
inradius = rand_num(outradius/1.5, outradius*(7.0/8.0));
midradius = (outradius + inradius) / 2;
midradius = (midradius == 0 ? 1 : midradius);
bubblesx = map.sec_width / midradius;
bubblesy = map.sec_height / midradius;
numbubbles = (bubblesx * bubblesy);
numbubbles = (numbubbles == 0 ? 1 : numbubbles);
for( r = 0; r < map.num_row; r++ )
{
for( c = 0; c < map.num_col; c++ )
{
for( b = 0; b < numbubbles; b++ )
{
rand_point_section_offset(¢erx, ¢ery, r, c, 0);
/*
*centerx *= (centerx / bubblesx);
*centery *= (centery / bubblesy);
*/
circlefill(map.map, centerx, centery, outradius, 255);
circlefill(map.map, centerx, centery, inradius, 0);
}
} /* for(c..) */
} /* for(r..) */
return;
}
void draw_selected_line(line l)
{
glEnable(GL_LINE_STIPPLE);
glLineWidth(8);
glLineStipple(1,0x00FF);
glColor3f(rand_num(),rand_num(),rand_num());
glBegin(GL_LINES);
glVertex2f(l.p1.x,l.p1.y);
glVertex2f(l.p2.x,l.p2.y);
glEnd();
glDisable(GL_LINE_STIPPLE);
glLineWidth(2);
}
void draw_point(point p)
{
/*glBegin(GL_POINTS);
glColor3f(rand_num(),rand_num(),rand_num());
glVertex2f(p.x,p.y);
glEnd();*/
int i;
float rad = 1;
glColor3f(rand_num(),rand_num(),rand_num());
glBegin(GL_TRIANGLE_FAN);
for(i=0 ; i<360 ; i++)
glVertex2f(p.x + rad * cos(DEG2RAD(i)), p.y + rad * sin(DEG2RAD(i)));
glEnd();
}
int
main(int argc, char *argv[])
{
int i, nloops;
unsigned int max_num;
Number x;
float m1 = 0.0, m2 = 0.0;
if (argc != 3) {
fprintf(stderr, "Usage: %s <max_num> <nloops>\n", argv[0]);
exit(1);
}
max_num = atoi(argv[1]);
nloops = atoi(argv[2]);
for (i = 0; i < nloops; i++) {
x = rand_num() % max_num;
m1 += ((float)x) / nloops;
m2 += ((float)x*x) / nloops;
}
printf(" Average: %lf\n", m1);
printf("Std.deviation: %lf\n", sqrt(m2 - m1*m1));
return 0;
}
int main(void)
{
// rand_num doesn't work until first world is initialized :(
GpWorld * w = gp_world_new();
for (int i = 0; i < TEST_SIZE; i++) {
data[i].x = rand_num() * 10000;
data[i].y = sqrt(data[i].x);
}
GpWorldConf default_conf = gp_world_conf_default();
default_conf.constant_func = &constant_func;
default_conf.evaluator = &eval;
default_conf.population_size = 50000;
default_conf.num_inputs = 1;
default_conf.min_program_length = 5;
default_conf.max_program_length = 30;
default_conf.num_registers = 2;
default_conf.minimize_fitness = 1;
default_conf.mutate_rate = 0.4;
GpWorldConf confs[4];
confs[0] = confs[1] = confs[2] = confs[3] = default_conf;
confs[0].num_registers = 1;
confs[1].num_registers = 2;
confs[2].num_registers = 3;
confs[3].num_registers = 4;
gp_test_configurations_secs(confs, sizeof(confs)/sizeof(GpWorldConf), 60, 4);
}
/*
* pickup:
* pick up a mine or grenade, with some probability of it exploding
*/
static void
pickup(PLAYER *pp, int y, int x, int prob, int obj)
{
int req;
/* Figure out how much ammo the player is trying to pick up: */
switch (obj) {
case MINE:
req = BULREQ;
break;
case GMINE:
req = GRENREQ;
break;
default:
#ifdef DIAGNOSTIC
abort();
#endif
return;
}
/* Does it explode? */
if (rand_num(100) < prob)
/* Ooooh, unlucky: (Boom) */
add_shot(obj, y, x, LEFTS, req, NULL, TRUE, pp->p_face);
else {
/* Safely picked it up. Add to player's ammo: */
pp->p_ammo += req;
ammo_update(pp);
}
}
/* create a test data set from the original data.
The test data can be used to calculate a final fitness
value, because it was not seen during training and so
provides an indication of how well the system has generalised */
static int create_test_data(float * training_data,
int * no_of_training_examples,
int fields_per_example,
float * test_data)
{
int i,j,k,index;
int no_of_test_examples = 0;
unsigned int random_seed = (unsigned int)time(NULL);
for (i = 0; i < MAX_TEST_EXAMPLES; i++) {
/* pick an example from the loaded data set */
index = rand_num(&random_seed)%(*no_of_training_examples);
/* increase the number of test examples */
for (j = 0; j < fields_per_example; j++) {
test_data[no_of_test_examples*fields_per_example + j] =
training_data[index*fields_per_example + j];
}
no_of_test_examples++;
/* reshuffle the original data set */
for (j = index+1; j < (*no_of_training_examples); j++) {
for (k = 0; k < fields_per_example; k++) {
training_data[(j-1)*fields_per_example + k] =
training_data[j*fields_per_example + k];
}
}
/* decrease the number of training data examples */
*no_of_training_examples = *no_of_training_examples - 1;
}
return no_of_test_examples;
}
void Utility::random_string(string &random, unsigned length)
{
CryptoPP::AutoSeededRandomPool RNG;
CryptoPP::Integer rand_num(RNG, 32);
for(unsigned i = 0; i < length; ++i)
{
unsigned num;
if(!rand_num.IsConvertableToLong())
num = std::numeric_limits<unsigned>::max() + static_cast<unsigned>(rand_num.AbsoluteValue().ConvertToLong());
else
num = static_cast<unsigned>(rand_num.AbsoluteValue().ConvertToLong());
num = num % 122;
if(48 > num)
num += 48;
if(57 < num && 65 > num)
num += 7;
if(90 < num && 97 > num)
num += 6;
random += static_cast<char>(num);
rand_num.Randomize(RNG, 32);
}
}
/*
* makeboots:
* Put the boots in the maze
*/
static void
makeboots(void)
{
int x, y;
PLAYER *pp;
if (conf_boots) {
do {
x = rand_num(WIDTH - 1) + 1;
y = rand_num(HEIGHT - 1) + 1;
} while (Maze[y][x] != SPACE);
Maze[y][x] = BOOT_PAIR;
}
for (pp = Boot; pp < &Boot[NBOOTS]; pp++)
pp->p_flying = -1;
}
/**
* Returns a pointer to a newly allocated array of length len
* containing random integer elements.
*/
int *rand_array(int len)
{
int *int_arr = (int*) malloc(len * sizeof(int));
for (int i = 0; i < len; i++) {
int_arr[i] = rand_num();
}
return int_arr;
}
/* generates a ramdom grid for given
site vacancy probability */
void grid_create(grid *grd, double prob)
{
int i, j;
for (i = 0; i < grd->height; i++)
for (j = 0; j < grd->width; j++)
grd->cells[grd->width * i + j] = (rand_num() < prob) ? SITE_OPEN : SITE_BLOCK;
}
/**
* @brief Randomly sets exclusion flags to cause units to drop out
* @param net Backprop neural net object
*/
static void bp_dropouts(bp * net)
{
int l,i,no_of_dropouts,hidden_units,n;
if (net->DropoutPercent==0) return;
/* total number of hidden units */
hidden_units = net->HiddenLayers * net->NoOfHiddens;
/* total number of dropouts */
no_of_dropouts = net->DropoutPercent*hidden_units/100;
/* set the exclusion flags */
for (n = 0; n < no_of_dropouts; n++) {
l = rand_num(&net->random_seed)%net->HiddenLayers;
i = rand_num(&net->random_seed)%net->NoOfHiddens;
net->hiddens[l][i]->excluded = 1;
}
}
// DESC: modifies health of patient if machine happens to harm user
// PRE: patient must have modify_help member function
// POST: patient.condition will be modified to reflect damage, returns
// nothing
void x_rayer::apply(patient & user)
{
// halve patient condition randomly. odds are 1 in DAMAGE_CHANCE
if (rand_num(0,X_RAYER_DAMAGE_CHANCE) == 0)
user.modify_phys_health(-user.get_phys_health()/2);
m_num_uses += 1;
return;
}
int*
gen_test_arr(int size) {
int* arr = (int*)malloc(size * sizeof(int));
assert(arr);
int i;
for (i = 0; i < size; ++i) {
arr[i] = rand_num(0, size);
}
return arr;
}
/**
* @brief Performs a single training step
* @param learner Deep learner object
* @returns 1=pretraining,2=final training,0=training complete,-1=no training data
*/
int deeplearndata_training(deeplearn * learner)
{
if (learner->training_data_samples == 0) {
return -1;
}
/* plot a graph showing training progress */
if (learner->training_ctr > learner->history_plot_interval) {
if (strlen(learner->history_plot_filename) > 0) {
deeplearn_plot_history(learner,
learner->history_plot_filename,
learner->history_plot_title,
1024, 480);
}
learner->training_ctr = 0;
}
learner->training_ctr++;
if ((learner->net->HiddenLayers > 1) &&
(learner->current_hidden_layer < learner->net->HiddenLayers)) {
/* index number of a random training sample */
int index = rand_num(&learner->net->random_seed)%learner->training_data_samples;
/* get the sample */
deeplearndata * sample = deeplearndata_get_training(learner, index);
deeplearn_set_inputs(learner, sample);
deeplearn_update(learner);
return 1;
}
if (learner->training_complete == 0) {
/* index number of a random training sample */
int index = rand_num(&learner->net->random_seed)%learner->training_data_labeled_samples;
/* get the sample */
deeplearndata * sample = deeplearndata_get_training_labeled(learner, index);
deeplearn_set_inputs(learner, sample);
deeplearn_set_outputs(learner, sample);
deeplearn_update(learner);
return 2;
}
return 0;
}
/*********************************************************
****************** Main Function ******************
*********************************************************/
int main(int agrc, char *agrv[])
{
int rt = 0; /* return value of function main */
srandom((unsigned int)time(NULL));
printf("rand num: \n");
while (rt++ < 100)
printf("%d ", rand_num(10,50));
//printf("rand num: %d\n", rand_num(10,50));
printf("\n");
return rt;
}
double monte_carlo(MINTEGP ap)
{
int m;
double a,b,r,x,s;
a=ap->a;b=ap->b;r=1.0;s=0.0;
for(m=0;m<ap->m;m++)
{
x=a+(b-a)*rand_num(&r);
s=s+(*ap->ptr)(x)/ap->m;
}
s=s*(b-a);
return s;
}
void test_matrix_rotate(void)
{
Matrix3f m1, m2, diff;
Vector3f r;
m1.identity();
m2.identity();
r.x = rand_num();
r.y = rand_num();
r.z = rand_num();
for (uint16_t i = 0; i<1000; i++) {
// old method
Matrix3f temp_matrix;
temp_matrix.a.x = 0;
temp_matrix.a.y = -r.z;
temp_matrix.a.z = r.y;
temp_matrix.b.x = r.z;
temp_matrix.b.y = 0;
temp_matrix.b.z = -r.x;
temp_matrix.c.x = -r.y;
temp_matrix.c.y = r.x;
temp_matrix.c.z = 0;
temp_matrix = m1 * temp_matrix;
m1 += temp_matrix;
// new method
m2.rotate(r);
// check they behave in the same way
diff = m1 - m2;
float err = diff.a.length() + diff.b.length() + diff.c.length();
if (err > 0) {
hal.console->printf("ERROR: i=%u err=%f\n", (unsigned)i, err);
}
}
}
/**
* Return a random direction.
* \return A random number representing a direction.
*/
int rand_dir() {
switch (rand_num(4)) {
case 0:
return LEFTS;
case 1:
return RIGHT;
case 2:
return BELOW;
case 3:
return ABOVE;
}
/* NOTREACHED */
return (-1);
}
static float evaluate_features(int trials,
gprcm_population * population,
int individual_index,
int custom_command)
{
int i,j,itt,n;
float error,diff=0,v,reference,fitness;
float dropout_rate = 0.2f;
gprcm_function * f = &population->individual[individual_index];
if (custom_command!=0) dropout_rate=0;
for (i = 0; i < trials; i++) {
/* clear the state */
gprcm_clear_state(f,
population->rows, population->columns,
population->sensors, population->actuators);
/* Randomly pick an example.
This discourages any ordering bias */
n = rand_num(&f->program.random_seed)%trials;
for (j=1;j<fields_per_example-3;j++) {
gprcm_set_sensor(f,j-1,
current_data_set[n*fields_per_example+j]);
}
for (itt = 0; itt < RUN_STEPS; itt++) {
/* run the program */
gprcm_run(f, population, dropout_rate, 0, 0);
}
/* how close is the output to the actual slump? */
for (j=0;j<3;j++) {
reference =
0.01f + fabs(current_data_set[n*fields_per_example+
fields_per_example-3+j]);
v = 0.01f + fabs(gprcm_get_actuator(f,j,
population->rows,
population->columns,
population->sensors));
error = fabs(v - reference)/reference;
diff += error*error;
}
}
diff = (float)sqrt(diff/(float)(trials*3))*100;
fitness = 100 - diff;
if (fitness<0) fitness=0;
return fitness;
}
//This function manages the game.
void play(void)
{
int total = 8;
int index = 0;
int i = 0;
int max = 0;
char str[10];
//create an array of strings
char words[9][6] = {"The", "quick", "brown", "fox", "jumps", "over", "the", "lazy", "dog"};
//create an array of numbers
int nums[9] = {0,1,2,3,4,5,6,7,8};
printf("\n\nWelcome to the Typing Speed Game!!\nType these words as fast as you can!");
printf("\nReady??\nPress ENTER to start.");
getchar();
struct timeval before, after;
gettimeofday(&before, NULL); //get the time at the start of the game
while (total > 0) //randomize the array of integers by swapping the last for a random number within the array, this is repeated for all the numbers.
{
index = rand_num(total);
max = nums[total];
nums[total] = nums[index]; //shuffle the two numbers
nums[index] = max;
--total; //decriment the total to leave the numbers that have been shuffled alone.
}
for (i = 0; i < 9; ++i) //Print the word that corresponds to the number in the array. User must match the word exactly to proceed to the next.
{
do
{
printf("\nWord #%d is %s: ",i+1, words[nums[i]]); //print the word
scanf("%9s", str); //input a word from the user
if (strcmp(str,words[nums[i]]) !=0)
printf("Incorrect. Try again.\n"); //if not a match, try again
} while(strcmp(str, words[nums[i]]) != 0);
}
gettimeofday(&after, NULL); //get the time at the end of the game
//Calculate the difference between the start and end time, then print it along with the time calculated in seconds.
printf("\n\nCorrect! Your time is: %.0lf sec, %.0lf usec\n" ,(time_diff(before, after) * 1.0e-6), time_diff(before, after));
}
void draw_line2(point p1,point p2)
{
if(light_transport_mode)
{
int i = 0;
float dist = distance_between_points(p1,p2);
float d0 = dist/8;
float d1 = rand_num()*d0;
float theta = atan2((p2.y-p1.y),(p2.x-p1.x));
float tot_dist = d1;
float ctheta = cos(theta),stheta = sin(theta);
p2.x = p1.x + d1*ctheta;
p2.y = p1.y + d1*stheta;
glBegin(GL_LINES);
glVertex2f(p1.x,p1.y);
glVertex2f(p2.x,p2.y);
while(1)
{
p1 = p2;
if(tot_dist+d0 > dist)
{
d0 = dist - tot_dist;
p2.x = p1.x + d0*ctheta;
p2.y = p1.y + d0*stheta;
if(i%2)
{
glVertex2f(p1.x,p1.y);
glVertex2f(p2.x,p2.y);
}
break;
}
p2.x = p1.x + d0*ctheta;
p2.y = p1.y + d0*stheta;
if(i%2)
{
glVertex2f(p1.x,p1.y);
glVertex2f(p2.x,p2.y);
}
tot_dist += d0;
i++;
}
glEnd();
return;
}
glBegin(GL_LINES);
glVertex2f(p1.x,p1.y);
glVertex2f(p2.x,p2.y);
glEnd();
}
static float evaluate_features(int trials,
gprcm_population * population,
int individual_index,
int mode)
{
int i,j,itt,n;
float diff=0,v,crime_rate,error,fitness;
float dropout_rate = 0.0f;
gprcm_function * f = &population->individual[individual_index];
for (i = 0; i < trials; i++) {
/* clear the state */
gprcm_clear_state(f,
population->rows, population->columns,
population->sensors, population->actuators);
/* randomly choose and example */
n = rand_num(&f->program.random_seed)%trials;
/* set the sensor values */
for (j=INITIAL_FIELDS;j<fields_per_example-1;j++) {
gprcm_set_sensor(f,j-INITIAL_FIELDS,
current_data_set[n*fields_per_example+j]);
}
for (itt = 0; itt < RUN_STEPS; itt++) {
/* run the program */
gprcm_run(f, population, dropout_rate, 0, 0);
}
/* how close is the output to the actual crime level? */
crime_rate =
0.0001f +
current_data_set[n*fields_per_example+fields_per_example-1];
v = fabs(gprcm_get_actuator(f,0,population->rows,
population->columns,
population->sensors));
error = (v - crime_rate)/crime_rate;
diff += error*error;
}
diff = (float)sqrt(diff/trials)*100;
fitness = 100 - diff;
if (fitness < 0) fitness=0;
return fitness;
}
int main(void)
{
GpWorld * world = gp_world_new();
for (int i = 0; i < TEST_SIZE; i++) {
data[i].x = rand_num() * 10000;
data[i].y = sqrt(data[i].x);
}
GpWorldConf conf = gp_world_conf_default();
conf.constant_func = &constant_func;
conf.evaluator = &eval;
conf.num_inputs = 1;
conf.minimize_fitness = 1;
gp_world_initialize(world, conf);
uint loops = 0;
uint total_steps = 0;
float ips = 0.0;
for (;;)
{
uint times = gp_world_evolve_secs(world, 1);
ips = (ips * loops + times) / (loops + 1);
total_steps += times;
printf("Best: %-9.2f Avg: %-9.2f Gens: %-3u Steps/sec: %-10.2f Avg Len: %-5.2f\n",
world->stats.best_fitness,
world->stats.avg_fitness,
world->stats.total_generations,
ips,
world->stats.avg_program_length);
loops++;
// Run for 60 seconds.
if (loops > 60) {
gp_program_export_python(stdout, world, &world->programs[0]);
return 0;
}
}
}
/* select the best individual to breed from */
void paintatron::select_best(int index)
{
int i;
for (i = 0; i < population; i++) {
if (i != index) {
if ((&sys.island[0])->fitness[i] > 20) {
(&sys.island[0])->fitness[i] /= 2;
}
else {
(&sys.island[0])->fitness[i] =
rand_num(&random_seed)%10000/100000.0f;
}
}
else {
(&sys.island[0])->fitness[i] = 99;
}
}
}
//
// `gp_world_optimize_test` will set up a sample problem and record
// program outputs for many inputs, then run a whole-world optimization,
// and rerun all the programs making sure the outputs haven't changed
//
void gp_world_optimize_test()
{
GpWorld * world = gp_world_new();
for (int i = 0; i < TEST_SIZE; i++)
_test_data[i] = rand_num() * 10000;
GpWorldConf conf = gp_world_conf_default();
conf.constant_func = &_test_constant_func;
conf.evaluator = &_test_eval;
conf.num_inputs = 1;
gp_world_initialize(world, conf);
uint i, j;
for (i = 0; i < world->conf.population_size; i++)
{
for (j = 0; j < TEST_SIZE; j++)
{
GpState state = gp_program_run(world, &world->programs[i], _test_data + j);
_test_out[j] = state.registers[0];
}
_remove_introns(world, &world->programs[i]);
for (j = 0; j < TEST_SIZE; j++)
{
GpState state = gp_program_run(world, &world->programs[i], _test_data + j);
if (state.registers[0] != _test_out[j])
printf("ERROR! Intron removal changed program output: %f vs %f\n",
state.registers[0], _test_out[j]);
}
}
gp_world_delete(world);
}