1 files changed, 141 insertions, 0 deletions
diff --git a/test/monniaux/genann/example4shorter.c b/test/monniaux/genann/example4shorter.c
new file mode 100644
index 00000000..ff4ce402
--- /dev/null
+++ b/test/monniaux/genann/example4shorter.c
@@ -0,0 +1,141 @@
+#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;
+}