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Commit 1aee4b75 authored by a.croix's avatar a.croix
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Refactoring. double[] change to List<Double>

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ressources/webshell_data_list.json 0 → 100644
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ressources/webshell_expected_list.json 0 → 100644
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View file @ 1aee4b75
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\ No newline at end of file
src/main/java/be/cylab/java/wowa/training/Convert.java 0 → 100644
+ 68
− 0
View file @ 1aee4b75
package be.cylab.java.wowa.training;
import com.owlike.genson.GenericType;
import com.owlike.genson.Genson;
import java.io.BufferedWriter;
import java.io.FileWriter;
import java.io.IOException;
import java.nio.charset.StandardCharsets;
import java.nio.file.Files;
import java.nio.file.Paths;
import java.util.ArrayList;
import java.util.List;
public class Convert {
public static void main(final String[] args) {
String data_json = null;
String expected_json = null;
try {
data_json = new String(Files.readAllBytes(Paths.get("./ressources/webshell_data.json")),
StandardCharsets.UTF_8);
expected_json = new String(Files.readAllBytes(Paths.get("./ressources/webshell_expected.json")),
StandardCharsets.UTF_8);
} catch (IOException e) {
e.printStackTrace();
}
Genson genson = new Genson();
List<double[]> data = genson.deserialize(data_json,
new GenericType<List<double[]>>() {
});
List<List<Double>> data_list = new ArrayList<>();
for (int i = 0; i < data.size(); i++) {
List<Double> vector_list = new ArrayList<>();
double[] vector = new double[data.get(i).length];
for (double el : vector) {
vector_list.add(el);
}
data_list.add(vector_list);
}
double[] expected = genson.deserialize(expected_json, double[].class);
List<Double> expected_list = new ArrayList<>();
for (double el : expected) {
expected_list.add(el);
}
String data_final = genson.serialize(data_list);
try {
BufferedWriter writer = new BufferedWriter(new FileWriter("./ressources/webshell_data_list.json"));
writer.write(data_final);
writer.close();
} catch (IOException e) {
e.printStackTrace();
}
String expected_final = genson.serialize(expected_list);
try {
BufferedWriter writer = new BufferedWriter(new FileWriter("./ressources/webshell_expected_list.json"));
writer.write(expected_final);
writer.close();
} catch (IOException e) {
e.printStackTrace();
}
}
}
src/main/java/be/cylab/java/wowa/training/SolutionAUC.java
+ 3
− 3
View file @ 1aee4b75
......@@ -16,9 +16,9 @@ public class SolutionAUC extends AbstractSolution {
}
final void computeScoreTo(
final List<double[]> data,
final double[] expected) {
this.fitness_score = 0;
final List<List<Double>> data,
final List<Double> expected) {
this.fitness_score = 0.0;
this.fitness_score = -(Utils.computeAUC(this, data, expected));
}
}
src/main/java/be/cylab/java/wowa/training/SolutionDistance.java
+ 1
− 1
View file @ 1aee4b75
......@@ -45,7 +45,7 @@ public class SolutionDistance extends AbstractSolution {
final List<Double> expected
) {
this.fitness_score = 0;
this.fitness_score = 0.0;
for (int i = 0; i < data.size(); i++) {
List<Double> vector = data.get(i);
Double target_value = expected.get(i);
......
src/main/java/be/cylab/java/wowa/training/Utils.java
+ 14
− 13
View file @ 1aee4b75
......@@ -178,9 +178,9 @@ final class Utils {
*/
public static double computeAUC(
final AbstractSolution solution,
final List<double[]> data,
final double[] expected) {
if (data.size() != expected.length) {
final List<List<Double>> data,
final List<Double> expected) {
if (data.size() != expected.size()) {
throw new IllegalArgumentException(
"Data and expected have different size");
}
......@@ -201,10 +201,10 @@ final class Utils {
*/
public static List<RocCoordinates> computeRocPoints(
final AbstractSolution solution,
final List<double[]> data,
final double[] expected,
final List<List<Double>> data,
final List<Double> expected,
final boolean save_on_csv) {
if (data.size() != expected.length) {
if (data.size() != expected.size()) {
throw new IllegalArgumentException(
"Data and expected have different size");
}
......@@ -228,7 +228,7 @@ final class Utils {
*/
private static double[] computeWOWAScoreWithData(
final AbstractSolution solution,
final List<double[]> data) {
final List<List<Double>> data) {
double[] score = new double[data.size()];
double[] weights_w
= convertListDoubleToArrayDouble(solution.getWeightsW());
......@@ -236,7 +236,8 @@ final class Utils {
= convertListDoubleToArrayDouble(solution.getWeightsP());
WOWA wowa = new WOWA(weights_w, weights_p);
for (int i = 0; i < data.size(); i++) {
score[i] = wowa.aggregate(data.get(i));
score[i] =
wowa.aggregate(convertListDoubleToArrayDouble(data.get(i)));
}
return score;
}
......@@ -246,12 +247,12 @@ final class Utils {
* @return
*/
private static boolean[] convertExpectedToBooleanArrayTrueAlert(
final double[] expected) {
boolean[] true_alert = new boolean[expected.length];
for (int i = 0; i < expected.length; i++) {
if (expected[i] == 1) {
final List<Double> expected) {
boolean[] true_alert = new boolean[expected.size()];
for (int i = 0; i < expected.size(); i++) {
if (expected.get(i) == 1) {
true_alert[i] = true;
} else if (expected[i] == 0) {
} else if (expected.get(i) == 0) {
true_alert[i] = false;
} else {
throw new IllegalStateException(
......
src/test/java/be/cylab/java/wowa/training/SolutionDistanceTest.java
+ 9
− 9
View file @ 1aee4b75
......@@ -24,8 +24,8 @@ class SolutionDistanceTest {
@org.junit.jupiter.api.Test
public void computeScoreTo() {
SolutionDistance solution = new SolutionDistance(5,1234);
List<double[]> data = generateData(20, 5);
double[] expected = generateExpected(20);
List<List<Double>> data = generateData(20, 5);
List<Double> expected = generateExpected(20);
solution.computeScoreTo(data, expected);
assertEquals(1.5925246410672433, solution.getFitnessScore());
}
......@@ -44,24 +44,24 @@ class SolutionDistanceTest {
}
static List<double[]> generateData(final int size, final int weight_number) {
static List<List<Double>> generateData(final int size, final int weight_number) {
Random rnd = new Random(5489);
List<double[]> data = new ArrayList<>();
List<List<Double>> data = new ArrayList<>();
for (int i = 0; i < size; i++) {
double[] vector = new double[weight_number];
List<Double> vector = new ArrayList<>();
for (int j = 0; j < weight_number; j++) {
vector[j] = rnd.nextDouble();
vector.add(rnd.nextDouble());
}
data.add(vector);
}
return data;
}
static double[] generateExpected(final int size) {
static List<Double> generateExpected(final int size) {
Random rnd = new Random(5768);
double[] expected = new double[size];
List<Double> expected = new ArrayList<>();
for (int i = 0; i < size; i++) {
expected[i] = rnd.nextDouble();
expected.add(rnd.nextDouble());
}
return expected;
}
......
src/test/java/be/cylab/java/wowa/training/TrainerTest.java
+ 13
− 13
View file @ 1aee4b75
......@@ -29,8 +29,8 @@ class TrainerTest {
AbstractSolution solution = new SolutionDistance(5, i);
population.add(solution);
}
List<double[]> data = generateData(100, 5);
double[] expected = generateExpected(100);
List<List<Double>> data = generateData(100, 5);
List<Double> expected = generateExpected(100);
List<AbstractSolution> computed_population = this.trainer.computeDistances(population, data, expected);
AbstractSolution bestSolution = this.trainer.findBestSolution(computed_population);
assertEquals(3.0482902643223135, bestSolution.getFitnessScore());
......@@ -55,8 +55,8 @@ class TrainerTest {
@Test
void testSelectParents() {
List<AbstractSolution> population = this.trainer.generateInitialPopulation(5, 100);
List<double[]> data = generateData(100, 5);
double[] expected = generateExpected(100);
List<List<Double>> data = generateData(100, 5);
List<Double> expected = generateExpected(100);
List<AbstractSolution> computed_population = this.trainer.computeDistances(population, data, expected);
AbstractSolution best_solution = this.trainer.findBestSolution(computed_population);
List<AbstractSolution> parents = this.trainer.selectParents(computed_population, 30, TrainerParameters.SELECTION_METHOD_TOS);
......@@ -82,8 +82,8 @@ class TrainerTest {
AbstractSolution solution = new SolutionDistance(5, 2*i);
population.add(solution);
}
List<double[]> data = generateData(100, 5);
double[] expected = generateExpected(100);
List<List<Double>> data = generateData(100, 5);
List<Double> expected = generateExpected(100);
population = this.trainer.computeDistances(population, data, expected);
List<AbstractSolution> parents = this.trainer.selectParents(population, 10, TrainerParameters.SELECTION_METHOD_RWS);
this.trainer.reproduce(parents.get(0), parents.get(1), parents, 1, 0.25875);
......@@ -116,24 +116,24 @@ class TrainerTest {
}
static List<double[]> generateData(final int size, final int weight_number) {
static List<List<Double>> generateData(final int size, final int weight_number) {
Random rnd = new Random(5489);
List<double[]> data = new ArrayList<>();
List<List<Double>> data = new ArrayList<>();
for (int i = 0; i < size; i++) {
double[] vector = new double[weight_number];
List<Double> vector = new ArrayList<>();
for (int j = 0; j < weight_number; j++) {
vector[j] = rnd.nextDouble();
vector.add(rnd.nextDouble());
}
data.add(vector);
}
return data;
}
static double[] generateExpected(final int size) {
static List<Double> generateExpected(final int size) {
Random rnd = new Random(5768);
double[] expected = new double[size];
List<Double> expected = new ArrayList<>();
for (int i = 0; i < size; i++) {
expected[i] = rnd.nextDouble();
expected.add(rnd.nextDouble());
}
return expected;
}
......
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