Initial commit

example1.c

@@ -0,0 +1,35 @@
+#include <stdio.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");
+
+    /* 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 < 300; ++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;
+}