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#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#include <string.h>
#include <math.h>
#include "genann.h"
#define VERIMAG
#ifdef VERIMAG
#include "../clock.h"
#endif
/* This example is to illustrate how to use GENANN.
* It is NOT an example of good machine learning techniques.
*/
const char *iris_data = "example/iris.data";
double *input, *class;
int samples;
const char *class_names[] = {"Iris-setosa", "Iris-versicolor", "Iris-virginica"};
void load_data() {
/* Load the iris data-set. */
FILE *in = fopen("example/iris.data", "r");
if (!in) {
printf("Could not open file: %s\n", iris_data);
exit(1);
}
/* Loop through the data to get a count. */
char line[1024];
while (!feof(in) && fgets(line, 1024, in)) {
++samples;
}
fseek(in, 0, SEEK_SET);
printf("Loading %d data points from %s\n", samples, iris_data);
/* Allocate memory for input and output data. */
input = malloc(sizeof(double) * samples * 4);
class = malloc(sizeof(double) * samples * 3);
/* Read the file into our arrays. */
int i, j;
for (i = 0; i < samples; ++i) {
double *p = input + i * 4;
double *c = class + i * 3;
c[0] = c[1] = c[2] = 0.0;
if (fgets(line, 1024, in) == NULL) {
perror("fgets");
exit(1);
}
char *split = strtok(line, ",");
for (j = 0; j < 4; ++j) {
p[j] = atof(split);
split = strtok(0, ",");
}
split[strlen(split)-1] = 0;
if (strcmp(split, class_names[0]) == 0) {c[0] = 1.0;}
else if (strcmp(split, class_names[1]) == 0) {c[1] = 1.0;}
else if (strcmp(split, class_names[2]) == 0) {c[2] = 1.0;}
else {
printf("Unknown class %s.\n", split);
exit(1);
}
/* printf("Data point %d is %f %f %f %f -> %f %f %f\n", i, p[0], p[1], p[2], p[3], c[0], c[1], c[2]); */
}
fclose(in);
}
int main(int argc, char *argv[])
{
printf("GENANN example 4.\n");
printf("Train an ANN on the IRIS dataset using backpropagation.\n");
#ifdef VERIMAG
srand(42);
#else
srand(time(0));
#endif
/* Load the data from file. */
load_data();
/* 4 inputs.
* 1 hidden layer(s) of 4 neurons.
* 3 outputs (1 per class)
*/
genann *ann = genann_init(4, 1, 4, 3);
int i, j;
#ifdef VERIMAG
int loops = 500;
#else
int loops = 5000;
#endif
/* Train the network with backpropagation. */
printf("Training for %d loops over data.\n", loops);
#ifdef VERIMAG
clock_prepare();
clock_start();
#endif
for (i = 0; i < loops; ++i) {
for (j = 0; j < samples; ++j) {
genann_train(ann, input + j*4, class + j*3, .01);
}
/* printf("%1.2f ", xor_score(ann)); */
}
int correct = 0;
for (j = 0; j < samples; ++j) {
const double *guess = genann_run(ann, input + j*4);
if (class[j*3+0] == 1.0) {if (guess[0] > guess[1] && guess[0] > guess[2]) ++correct;}
else if (class[j*3+1] == 1.0) {if (guess[1] > guess[0] && guess[1] > guess[2]) ++correct;}
else if (class[j*3+2] == 1.0) {if (guess[2] > guess[0] && guess[2] > guess[1]) ++correct;}
else {printf("Logic error.\n"); exit(1);}
}
#ifdef VERIMAG
clock_stop();
#endif
printf("%d/%d correct (%0.1f%%).\n", correct, samples, (double)correct / samples * 100.0);
#ifdef VERIMAG
print_total_clock();
#endif
genann_free(ann);
free(input);
free(class);
return 0;
}
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