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#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <math.h>
#include "genann.h"
int main(int argc, char *argv[])
{
printf("GENANN example 2.\n");
printf("Train a small ANN to the XOR function using random search.\n");
srand(time(0));
/* Input and expected out data for the XOR function. */
const double input[4][2] = {{0, 0}, {0, 1}, {1, 0}, {1, 1}};
const double output[4] = {0, 1, 1, 0};
int i;
/* New network with 2 inputs,
* 1 hidden layer of 2 neurons,
* and 1 output. */
genann *ann = genann_init(2, 1, 2, 1);
double err;
double last_err = 1000;
int count = 0;
do {
++count;
if (count % 1000 == 0) {
/* We're stuck, start over. */
genann_randomize(ann);
last_err = 1000;
}
genann *save = genann_copy(ann);
/* Take a random guess at the ANN weights. */
for (i = 0; i < ann->total_weights; ++i) {
ann->weight[i] += ((double)rand())/RAND_MAX-0.5;
}
/* See how we did. */
err = 0;
err += pow(*genann_run(ann, input[0]) - output[0], 2.0);
err += pow(*genann_run(ann, input[1]) - output[1], 2.0);
err += pow(*genann_run(ann, input[2]) - output[2], 2.0);
err += pow(*genann_run(ann, input[3]) - output[3], 2.0);
/* Keep these weights if they're an improvement. */
if (err < last_err) {
genann_free(save);
last_err = err;
} else {
genann_free(ann);
ann = save;
}
} while (err > 0.01);
printf("Finished in %d loops.\n", count);
/* Run the network and see what it predicts. */
printf("Output for [%1.f, %1.f] is %1.f.\n", input[0][0], input[0][1], *genann_run(ann, input[0]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[1][0], input[1][1], *genann_run(ann, input[1]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[2][0], input[2][1], *genann_run(ann, input[2]));
printf("Output for [%1.f, %1.f] is %1.f.\n", input[3][0], input[3][1], *genann_run(ann, input[3]));
genann_free(ann);
return 0;
}