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#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include "genann.h"
int main(int argc, char *argv[])
{
printf("GENANN example 1.\n");
printf("Train a small ANN to the XOR function using backpropagation.\n");
/* This will make the neural network initialize differently each run. */
/* If you don't get a good result, try again for a different result. */
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);
/* Train on the four labeled data points many times. */
for (i = 0; i < 500; ++i) {
genann_train(ann, input[0], output + 0, 3);
genann_train(ann, input[1], output + 1, 3);
genann_train(ann, input[2], output + 2, 3);
genann_train(ann, input[3], output + 3, 3);
}
/* 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;
}