diff --git a/src/main/java/be/cylab/java/wowa/training/Trainer.java b/src/main/java/be/cylab/java/wowa/training/Trainer.java
index 734fdf1834c75ed8e13ba2b98fcb3818f73a375c..01ff230160e5c5146e702b8f4c128f05b6b50000 100644
--- a/src/main/java/be/cylab/java/wowa/training/Trainer.java
+++ b/src/main/java/be/cylab/java/wowa/training/Trainer.java
@@ -166,7 +166,7 @@ public class Trainer {
int data_size = expected.size();
for (int i = 0; i < data_size; i++) {
if (expected.get(i) == 1) {
- for (int j = 0; j < increase_ratio; j++) {
+ for (int j = 0; j < increase_ratio - 1; j++) {
expected.add(expected.get(i));
data.add(data.get(i));
}
@@ -215,6 +215,17 @@ public class Trainer {
}
fold_dataset.add(tmp);
}
+ int fold_counter = 0;
+ while (dataset.getLength() > 0) {
+ int i = Utils.randomInteger(0, dataset.getLength() - 1);
+ fold_dataset.get(fold_counter).addElementInDataset(dataset, i);
+ dataset.removeElementInDataset(i);
+ if (fold_counter == fold_dataset.size() - 1) {
+ fold_counter = 0;
+ } else {
+ fold_counter++;
+ }
+ }
return fold_dataset;
}
diff --git a/src/main/java/be/cylab/java/wowa/training/Utils.java b/src/main/java/be/cylab/java/wowa/training/Utils.java
index 52d1c8c28ff38d9858f67d1bb0dbba01589891b2..362b56ec110d0910d96e3554caea837ae2975ee3 100644
--- a/src/main/java/be/cylab/java/wowa/training/Utils.java
+++ b/src/main/java/be/cylab/java/wowa/training/Utils.java
@@ -111,8 +111,10 @@ final class Utils {
* @return
*/
static int randomInteger(final int min, final int max) {
- if (min >= max) {
+ if (min > max) {
throw new IllegalArgumentException("Max must be greater then min");
+ } else if (min == max) {
+ return min;
}
Random rnd = new Random();
return rnd.nextInt((max - min) + 1) + min;
diff --git a/src/test/java/be/cylab/java/wowa/training/TrainerTest.java b/src/test/java/be/cylab/java/wowa/training/TrainerTest.java
index bce9edb6c521cb674ac64511bef4deaae76209ff..fd5d815f5260415d4c216c6a49134e66f358bf64 100644
--- a/src/test/java/be/cylab/java/wowa/training/TrainerTest.java
+++ b/src/test/java/be/cylab/java/wowa/training/TrainerTest.java
@@ -27,29 +27,30 @@ class TrainerTest {
List<List<Double>> data = generateData(100, 5);
int original_data_set_size = data.size();
List<Double> expected = generateExpectedBinaryClassification(100);
- int increase_ration = 10;
- double number_of_true_alert = Utils.sumListElements(expected);
- TrainingDataset ds = trainer.increaseTrueAlert(data, expected, increase_ration);
+ int increase_ratio = 10;
+ int number_of_true_alert = (int)(double)Utils.sumListElements(expected);
+ int number_of_no_alert = original_data_set_size - number_of_true_alert;
+ TrainingDataset ds = trainer.increaseTrueAlert(data, expected, increase_ratio);
//Check if the length of the dataset us correct
//increase_ration * number_of_true_alert + expected.size()
- assertEquals(630, ds.getLength());
+ assertEquals(number_of_true_alert * increase_ratio + number_of_no_alert, ds.getLength());
//Check the number of true_alert in the dataset
//increase_ration * number_of_true_alert + number_of_true_alert
- assertEquals(583, (double)Utils.sumListElements(ds.getExpected()));
+ assertEquals(number_of_true_alert * increase_ratio, (double)Utils.sumListElements(ds.getExpected()));
//Check if each rue alert elements are present (increase_ratio time) in he dataset
//Here, we check if each true alert element is present 10 more times than the original one
for (int i = 0; i < expected.size(); i++) {
if (ds.getExpected().get(i) == 1.0) {
int cnt = 0;
- for (int j = original_data_set_size; j < ds.getLength(); j++) {
+ for (int j = 0; j < ds.getLength(); j++) {
if (ds.getExpected().get(i) == ds.getExpected().get(j)) {
cnt++;
}
}
- assertEquals(increase_ration, cnt);
+ assertEquals(increase_ratio, cnt);
}
}
@@ -57,13 +58,29 @@ class TrainerTest {
@Test
void testPrepareFolds() {
- List<List<Double>> data = generateData(100,5);
- List<Double> expected = generateExpectedBinaryClassification(100);
- int increase_ratio = 5;
- int fold_number = 5;
+ int number_of_elements = 100;
+ List<List<Double>> data = generateData(number_of_elements,5);
+ List<Double> expected = generateExpectedBinaryClassification(number_of_elements);
+ int increase_ratio = 3;
+ int fold_number = 10;
+ int number_of_alert = (int)(double)Utils.sumListElements(expected);
+ int number_of_no_alert = number_of_elements - number_of_alert;
TrainingDataset ds = trainer.increaseTrueAlert(data, expected, increase_ratio);
List<TrainingDataset> folds = trainer.prepareFolds(ds, fold_number);
assertEquals(fold_number, folds.size());
+ for (int i = 0; i < folds.size(); i++) {
+ assertTrue(folds.get(i).getLength()
+ == (number_of_alert * increase_ratio + number_of_no_alert)
+ / fold_number || folds.get(i).getLength()
+ == 1 + (number_of_alert * increase_ratio + number_of_no_alert)
+ / fold_number);
+ assertTrue(Utils.sumListElements(folds.get(i).getExpected())
+ == ((number_of_alert * increase_ratio) / fold_number) ||
+ Utils.sumListElements(folds.get(i).getExpected())
+ == 1 + ((number_of_alert * increase_ratio) / fold_number)
+ || Utils.sumListElements(folds.get(i).getExpected())
+ == 2 + ((number_of_alert * increase_ratio) / fold_number));
+ }
}
@Test
diff --git a/src/test/java/be/cylab/java/wowa/training/UtilsTest.java b/src/test/java/be/cylab/java/wowa/training/UtilsTest.java
index 0f41032d3946fb0900ab68a6450d678f4cb530ce..57b7920d97083f6fe536a7590894db2e2b60bb90 100644
--- a/src/test/java/be/cylab/java/wowa/training/UtilsTest.java
+++ b/src/test/java/be/cylab/java/wowa/training/UtilsTest.java
@@ -162,8 +162,8 @@ class UtilsTest {
int a = Utils.randomInteger(0, 500);
assertTrue(a > 0);
assertTrue(a < 500);
+ assertEquals(45, Utils.randomInteger(45, 45));
assertThrows(IllegalArgumentException.class, () -> {Utils.randomInteger(125, 120);});
- assertThrows(IllegalArgumentException.class, () -> {Utils.randomInteger(125, 125);});
int b = Utils.randomInteger(45,46);
assertTrue(b == 45 || b == 46);