diff --git a/CSV.csv b/CSV.csv
new file mode 100644
index 0000000000000000000000000000000000000000..286e9fca1d5e851808442665dd0d84e5a80475a5
--- /dev/null
+++ b/CSV.csv
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+0.018917239937643403,0.0
+0.2547649189407494,0.0
+0.27274113189208743,0.0
+0.12629162921010284,0.0
+0.4340685187857748,0.0
+0.12007801110726228,0.0
+0.0850530905358166,0.0
+0.06740740090833462,0.0
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+0.26587385760759275,0.0
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+0.2747801965696059,0.0
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+0.06161674467386904,0.0
+0.16090772416746052,0.0
+0.05042932979966343,0.0
+0.13501821238649314,0.0
+0.07897424608426207,0.0
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+0.06168088923973655,0.0
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+0.06477684238925725,0.0
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+0.0480620935120012,0.0
+0.11985329606505753,0.0
+0.1271985412810265,0.0
+0.19753380591780448,0.0
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+0.17074593384098394,0.0
+0.09867293572120278,0.0
+0.25811870234436096,0.0
+0.16475655908888637,0.0
+0.5229311248230533,0.0
+0.33774566585726695,0.0
+0.005818517623509512,0.0
+0.41440212754893574,0.0
+0.09177830752537144,0.0
+0.1503216548857295,0.0
+0.03724636501332066,0.0
+0.15189533114435932,0.0
diff --git a/pom.xml b/pom.xml
index 3db99b868ce937228db506f39f03f93b4ca07baa..90782bc9ebdacc42a0f07d381a8ab0a0332e073f 100644
--- a/pom.xml
+++ b/pom.xml
@@ -59,20 +59,6 @@
             <scope>test</scope>
         </dependency>
 
-        <dependency>
-            <groupId>org.junit.jupiter</groupId>
-            <artifactId>junit-jupiter-params</artifactId>
-            <version>5.3.1</version>
-            <scope>test</scope>
-        </dependency>
-
-        <dependency>
-            <groupId>org.junit.jupiter</groupId>
-            <artifactId>junit-jupiter-engine</artifactId>
-            <version>5.3.1</version>
-            <scope>test</scope>
-        </dependency>
-
         <dependency>
             <groupId>info.debatty</groupId>
             <artifactId>java-aggregation</artifactId>
@@ -95,44 +81,72 @@
         <dependency>
             <groupId>be.cylab</groupId>
             <artifactId>java-roc</artifactId>
-            <version>0.0.4</version>
+            <version>0.0.5</version>
         </dependency>
 
         <dependency>
-            <groupId>org.datavec</groupId>
-            <artifactId>datavec-api</artifactId>
-            <version>0.9.1</version>
+            <groupId>org.knowm.xchart</groupId>
+            <artifactId>xchart</artifactId>
+            <version>3.5.4</version>
         </dependency>
 
+
         <dependency>
-            <groupId>org.nd4j</groupId>
-            <artifactId>nd4j-api</artifactId>
-            <version>0.9.1</version>
+            <groupId>org.datavec</groupId>
+            <artifactId>datavec-api</artifactId>
+            <version>1.0.0-beta5</version>
         </dependency>
 
         <dependency>
             <groupId>org.nd4j</groupId>
-            <artifactId>nd4j-native</artifactId>
-            <version>0.9.1</version>
+            <artifactId>nd4j-api</artifactId>
+            <version>1.0.0-beta5</version>
         </dependency>
 
         <dependency>
             <groupId>org.deeplearning4j</groupId>
             <artifactId>deeplearning4j-nn</artifactId>
-            <version>0.9.1</version>
+            <version>1.0.0-beta5</version>
         </dependency>
 
 
         <dependency>
             <groupId>org.deeplearning4j</groupId>
             <artifactId>deeplearning4j-core</artifactId>
-            <version>0.9.1</version>
+            <version>1.0.0-beta5</version>
         </dependency>
 
+        <dependency>
+            <groupId>org.nd4j</groupId>
+            <artifactId>nd4j-native-platform</artifactId>
+            <version>1.0.0-beta5</version>
+        </dependency>
+
+
         <dependency>
             <groupId>org.nd4j</groupId>
             <artifactId>nd4j-common</artifactId>
-            <version>0.9.1</version>
+            <version>1.0.0-beta5</version>
+        </dependency>
+
+
+        <dependency>
+            <groupId>org.slf4j</groupId>
+            <artifactId>slf4j-api</artifactId>
+            <version>2.0.0-alpha1</version>
+        </dependency>
+
+        <!-- Dependencies to solve maven-dependency issues-->
+        <dependency>
+            <groupId>org.deeplearning4j</groupId>
+            <artifactId>deeplearning4j-datavec-iterators</artifactId>
+            <version>1.0.0-beta5</version>
+        </dependency>
+
+        <dependency>
+            <groupId>org.deeplearning4j</groupId>
+            <artifactId>deeplearning4j-utility-iterators</artifactId>
+            <version>1.0.0-beta5</version>
         </dependency>
 
 
@@ -231,9 +245,8 @@
                             <failOnWarning>true</failOnWarning>
                             <ignoreNonCompile>false</ignoreNonCompile>
                             <ignoredDependencies>
-                                <ignoreDependency>org.junit.jupiter:junit-jupiter-engine:*</ignoreDependency>
-                                <ignoreDependency>org.junit.jupiter:junit-jupiter-params:*</ignoreDependency>
-                                <ignoredDependency>org.nd4j:nd4j-native</ignoredDependency>
+                                <ignoreDependency>org.deeplearning4j:deeplearning4j-core</ignoreDependency>
+                                <ignoredDependency>org.nd4j:nd4j-native-platform</ignoredDependency>
                             </ignoredDependencies>
                         </configuration>
                         <goals>
diff --git a/src/main/java/be/cylab/java/wowa/training/AbstractSolution.java b/src/main/java/be/cylab/java/wowa/training/AbstractSolution.java
index 8c169447e76cdb7dd87bb1bb148640d1f5c7cd48..a5ec8516cb9296b2000d04d2846d6bb80abb46fd 100644
--- a/src/main/java/be/cylab/java/wowa/training/AbstractSolution.java
+++ b/src/main/java/be/cylab/java/wowa/training/AbstractSolution.java
@@ -1,7 +1,8 @@
 package be.cylab.java.wowa.training;
 
+import be.cylab.java.roc.CurveCoordinates;
+import be.cylab.java.roc.PRCurve;
 import be.cylab.java.roc.Roc;
-import be.cylab.java.roc.RocCoordinates;
 import info.debatty.java.aggregation.WOWA;
 
 import java.text.DateFormat;
@@ -105,7 +106,7 @@ public abstract class AbstractSolution
      * @param expected
      * @return Have to be test in each child class and not in the absract class.
      */
-    final List<RocCoordinates> computeRocPoints(
+    final List<CurveCoordinates> computeRocPoints(
             final List<List<Double>> data,
             final List<Double> expected,
             final boolean save_on_csv) {
@@ -113,11 +114,11 @@ public abstract class AbstractSolution
             throw new IllegalArgumentException(
                     "Data and expected have different size");
         }
-        boolean[] true_alert
+        double[] true_alert
                 = Utils.convertExpectedToBooleanArrayTrueAlert(expected);
         double[] score = this.computeWOWAScoreWithData(data);
         Roc roc = new Roc(score, true_alert);
-        List<RocCoordinates> coordinates = roc.computeRocPoints();
+        List<CurveCoordinates> coordinates = roc.computeRocPoints();
         if (save_on_csv) {
             DateFormat format = new SimpleDateFormat("yyyyMMdd_HH:mm:ss");
             Date date = new Date();
@@ -136,21 +137,57 @@ public abstract class AbstractSolution
      */
     final double computeAUC(
             final List<List<Double>> data,
-            final List<Double> expected) {
+            final List<Double> expected,
+            final String filename) {
         if (data.size() != expected.size()) {
             throw new IllegalArgumentException(
                     "Data and expected have different size");
         }
         //Convert the array with expected values (0 or 1) to a boolean array.
-        boolean[] true_alert
+        double[] true_alert
                 = Utils.convertExpectedToBooleanArrayTrueAlert(expected);
 
         //For each elements in dataset, compute the wowa function.
         double[] score = this.computeWOWAScoreWithData(data);
         Roc roc = new Roc(score, true_alert);
+        if (filename != null) {
+            roc.computeRocPointsAndGenerateCurve(filename);
+            return roc.computeAUC();
+        }
+
         return roc.computeAUC();
     }
 
+    /**
+     * Method to compute PR-AUC and generate a figure with the P-R curve.
+     * @param data
+     * @param expected
+     * @param filename
+     * @return
+     */
+    final double computePRAUC(final List<List<Double>> data,
+                              final List<Double> expected,
+                              final String filename) {
+        if (data.size() != expected.size()) {
+            throw new IllegalArgumentException(
+                    "Data and expected have different size");
+        }
+        //Convert the array with expected values (0 or 1) to a boolean array.
+        double[] true_alert
+                = Utils.convertExpectedToBooleanArrayTrueAlert(expected);
+
+        //For each elements in dataset, compute the wowa function.
+        double[] score = this.computeWOWAScoreWithData(data);
+        PRCurve pr = new PRCurve(score, true_alert);
+        if (filename != null) {
+            pr.computePrcPointsAndGenerateCurve(filename);
+            return pr.computePRAUC();
+        }
+
+        return pr.computePRAUC();
+    }
+
+
     /**
      * Method to compute AUC with filename as arugument.
      *
@@ -160,12 +197,13 @@ public abstract class AbstractSolution
      */
     public final double computeAUC(
             final String filename_data,
-            final String filename_expected) {
+            final String filename_expected,
+            final String filename_curve) {
         List<List<Double>> data
                 = Utils.convertJsonToDataForTrainer(filename_data);
         List<Double> expected
                 = Utils.convertJsonToExpectedForTrainer(filename_expected);
-        return computeAUC(data, expected);
+        return computeAUC(data, expected, filename_curve);
     }
 
     /**
diff --git a/src/main/java/be/cylab/java/wowa/training/Example.java b/src/main/java/be/cylab/java/wowa/training/Example.java
index 9b013ce2d2dfd8b8740046ea1cb6f166f4b67fcd..cdc423fedf5b986aeaac6b34bb90050b8a237f6a 100644
--- a/src/main/java/be/cylab/java/wowa/training/Example.java
+++ b/src/main/java/be/cylab/java/wowa/training/Example.java
@@ -1,13 +1,17 @@
 package be.cylab.java.wowa.training;
 
 
+import java.io.FileOutputStream;
+import java.io.IOException;
+import java.io.OutputStreamWriter;
+import java.nio.charset.StandardCharsets;
 import java.util.HashMap;
-import java.util.Map;
 import java.util.logging.Level;
 import java.util.logging.Logger;
 import java.util.logging.SimpleFormatter;
 import java.util.logging.StreamHandler;
 
+
 /**
  * @author CYLAB
  */
@@ -21,8 +25,9 @@ public final class Example {
      * Run algorithm with webshell dataset.
      *
      * @param args
+     * @throws IOException Throw error for file
      */
-    public static void main(final String[] args) {
+    public static void main(final String[] args) throws IOException {
         int population_size = Integer.parseInt(args[0]);
         int crossover_rate = Integer.parseInt(args[1]);
         int mutation_rate = Integer.parseInt(args[2]);
@@ -47,6 +52,8 @@ public final class Example {
             throw new IllegalArgumentException(
                     "Initialization must be RANDOM or QUASI_RANDOM");
         }
+        int fold_number = Integer.parseInt(args[6]);
+        int increase_ratio = Integer.parseInt(args[7]);
 
         String data_file = "./ressources/webshell_data_new_version.json";
         String expected_file
@@ -64,29 +71,70 @@ public final class Example {
                 selection_method,
                 generation_population_method);
         Trainer trainer = new Trainer(parameters, new SolutionDistance(5));
-
-        long start_time = System.currentTimeMillis();
+/*
         AbstractSolution solution = trainer.run(
                 data_file,
                 expected_file);
         System.out.println(solution);
-        long end_time = System.currentTimeMillis();
-        logger.log(Level.INFO, "Execution time : "
-                + (end_time - start_time) / 1000 + " seconds");
 
         System.out.println("Run Cross validation");
-        HashMap<AbstractSolution, Double> solutions = trainer.runKFold(
+ */
+        long start_time = System.currentTimeMillis();
+        HashMap<AbstractSolution, double[]> solutions = trainer.runKFold(
                 data_file,
                 expected_file,
-                10,
-                2);
+                fold_number,
+                increase_ratio);
+        long end_time = System.currentTimeMillis();
+        System.out.println("Execution time : "
+                + (end_time - start_time) / 1000 + " seconds");
 
         double average_auc = 0;
-        for (Map.Entry val : solutions.entrySet()) {
-            System.out.println(val);
-            average_auc = average_auc + (double) val.getValue();
+        double average_aucpr = 0;
+        for (double[] d : solutions.values()) {
+            average_auc = average_auc + d[0];
+            average_aucpr = average_aucpr + d[1];
+        }
+        try (OutputStreamWriter writer = new OutputStreamWriter(
+                new FileOutputStream("Synthesis_average_AUC.txt", true),
+                StandardCharsets.UTF_8)) {
+            writer.write("Population size : " + population_size
+                    + " | Crossover rate : " + crossover_rate
+                    + " | Mutation Rate : " + mutation_rate
+                    + " | Selection method : " + args[4]
+                    + " | Genaration population method : " + args[5]
+                    + " | Average AUC = "
+                    + (average_auc / fold_number)
+                    + " | Average AUC - PR = "
+                    + (average_aucpr / fold_number) + "\n");
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+
+
+/*
+        AbstractSolution sol = trainer.run(data_file, expected_file);
+        List<List<Double>> data = Utils.convertJsonToDataForTrainer(
+        data_file);
+        List<Double> expected = Utils.convertJsonToExpectedForTrainer(
+        expected_file);
+        WOWA wowa = new WOWA(
+                Utils.convertListDoubleToArrayDouble(sol.getWeightsW()),
+                Utils.convertListDoubleToArrayDouble(sol.getWeightsP()));
+        FileWriter csv = new FileWriter("CSV.csv");
+        for (int i = 0; i < expected.size(); i++) {
+            double[] data_array = Utils.convertListDoubleToArrayDouble(
+            data.get(i));
+            double result = wowa.aggregate(data_array);
+            double ex = expected.get(i);
+            csv.append(Double.toString(result));
+            csv.append(",");
+            csv.append(Double.toString(ex));
+            csv.append("\n");
         }
-        logger.log(Level.INFO, "Average AUC : " + average_auc / 10);
+        csv.flush();
+        csv.close();
+*/
 
     }
 
diff --git a/src/main/java/be/cylab/java/wowa/training/HyperParameters.java b/src/main/java/be/cylab/java/wowa/training/HyperParameters.java
index 9915e6e3f926f7bb17b4cfdb4290aefbf5c0be8a..2e941365f6cd7a86baade57da0197b783a54d620 100644
--- a/src/main/java/be/cylab/java/wowa/training/HyperParameters.java
+++ b/src/main/java/be/cylab/java/wowa/training/HyperParameters.java
@@ -2,16 +2,20 @@ package be.cylab.java.wowa.training;
 
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.nd4j.linalg.activations.Activation;
+import org.slf4j.Logger;
+
 
 /**
  * Parameters of the NeuralNetwork class.
  */
 public class HyperParameters {
+    private Logger logger;
     private int neurons_number;
     private double learning_rate;
     private OptimizationAlgorithm algorithm;
     private Activation activation_function;
-    private double percent_test_train;
+    private int batch_size;
+    private int epoch_number;
 
     /**
      * Default constructor.
@@ -19,35 +23,22 @@ public class HyperParameters {
      * @param learning_rate
      * @param algorithm
      * @param activation_function
-     * @param percent_test_train
      */
     public HyperParameters(
+            final Logger logger,
             final int neurons_number,
             final double learning_rate,
             final OptimizationAlgorithm algorithm,
             final Activation activation_function,
-            final double percent_test_train) {
+            final int batch_size,
+            final int epoch_number) {
+        setLogger(logger);
         setNeuronsNumber(neurons_number);
         setLearningRate(learning_rate);
         setAlgorithm(algorithm);
         setActivationFunction(activation_function);
-        setPercentTestTrain(percent_test_train);
-    }
-
-    /**
-     * Constructor without percent_train_test value.
-     * @param neurons_number
-     * @param learning_rate
-     * @param algorithm
-     * @param activation_function
-     */
-    public HyperParameters(
-            final int neurons_number,
-            final double learning_rate,
-            final OptimizationAlgorithm algorithm,
-            final Activation activation_function) {
-        this(neurons_number, learning_rate, algorithm,
-                activation_function, 99);
+        setBatchSize(batch_size);
+        setEpochNumber(epoch_number);
     }
 
     /**
@@ -82,14 +73,6 @@ public class HyperParameters {
         return activation_function;
     }
 
-    /**
-     * Getter for percent_test_train.
-     * @return
-     */
-    public final double getPercentTestTrain() {
-        return percent_test_train;
-    }
-
     /**
      * @param neurons_number
      */
@@ -128,13 +111,50 @@ public class HyperParameters {
     }
 
     /**
-     * @param percent_test_train
+     * @param epoch_number
      */
-    public final void setPercentTestTrain(final double percent_test_train) {
-        if (percent_test_train <= 0 || percent_test_train >= 100) {
-            throw new IllegalArgumentException(
-                    "Percentage of train must be between 0 and 100");
+    public final void setEpochNumber(final int epoch_number) {
+        this.epoch_number = epoch_number;
+    }
+
+    /**
+     * @return
+     */
+    public final int getEpochNumber() {
+        return  this.epoch_number;
+    }
+
+    /**
+     * Geter for batchsize.
+     * @return
+     */
+    public final int getBatchSize() {
+        return batch_size;
+    }
+
+    /**
+     * Setter for batchsize.
+     * @param batch_size
+     */
+    public final void setBatchSize(final int batch_size) {
+        if (batch_size < 1) {
+            throw  new IllegalArgumentException("Batch size must be equal"
+                    + "or greater than 1");
         }
-        this.percent_test_train = percent_test_train;
+        this.batch_size = batch_size;
+    }
+
+    /**
+     * @return
+     */
+    public final Logger getLogger() {
+        return logger;
+    }
+
+    /**
+     * @param logger
+     */
+    public final void setLogger(final Logger logger) {
+        this.logger = logger;
     }
 }
diff --git a/src/main/java/be/cylab/java/wowa/training/MainDL4J.java b/src/main/java/be/cylab/java/wowa/training/MainDL4J.java
index b5be71399cba6f65f3343a53a08b8ae7edc0ccb9..3a3f141e21227fa2c3c73b538a78d37e4b1d7f0e 100644
--- a/src/main/java/be/cylab/java/wowa/training/MainDL4J.java
+++ b/src/main/java/be/cylab/java/wowa/training/MainDL4J.java
@@ -3,6 +3,8 @@ package be.cylab.java.wowa.training;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.nd4j.linalg.activations.Activation;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 
 import java.io.FileOutputStream;
 import java.io.IOException;
@@ -27,24 +29,27 @@ public final class MainDL4J {
      * @param args
      */
     public static void main(final String[] args) {
-        int begin_neuron_number = Integer.parseInt(args[0]);
-        int neuron_number_end = Integer.parseInt(args[1]);
-        String data_file = args[5];
-        String expected_file = args[6];
-        double learning_rate = Double.parseDouble(args[2]);
+        int neuron_number = Integer.parseInt(args[0]);
+        String data_file = args[4];
+        String expected_file = args[5];
+        double learning_rate = Double.parseDouble(args[1]);
         OptimizationAlgorithm optimization_algorithm;
         Activation activation_function;
-        int fold_number = 10;
-        int increase_ratio = 10;
+        int fold_number = Integer.parseInt(args[6]);
+        int increase_ratio = Integer.parseInt(args[7]);
+        int batch_size = Integer.parseInt(args[8]);
+        int epoch_number = Integer.parseInt(args[9]);
 
-        if (args[3].matches("CONJUGATE_GRADIENT")) {
+
+
+        if (args[2].matches("CONJUGATE_GRADIENT")) {
             optimization_algorithm = OptimizationAlgorithm.CONJUGATE_GRADIENT;
-        } else if (args[3].matches("LBFGS")) {
+        } else if (args[2].matches("LBFGS")) {
             optimization_algorithm = OptimizationAlgorithm.LBFGS;
-        } else if (args[3].matches("LINE_GRADIENT_DESCENT")) {
+        } else if (args[2].matches("LINE_GRADIENT_DESCENT")) {
             optimization_algorithm
                     = OptimizationAlgorithm.LINE_GRADIENT_DESCENT;
-        } else if (args[3].matches("STOCHASTIC_GRADIENT_DESCENT")) {
+        } else if (args[2].matches("STOCHASTIC_GRADIENT_DESCENT")) {
             optimization_algorithm
                     = OptimizationAlgorithm.STOCHASTIC_GRADIENT_DESCENT;
         } else {
@@ -52,48 +57,54 @@ public final class MainDL4J {
                     "Not correct optimization algorithm");
         }
 
-        if (args[4].matches("RELU")) {
+        if (args[3].matches("RELU")) {
             activation_function = Activation.RELU;
-        } else if (args[4].matches("SIGMOID")) {
+        } else if (args[3].matches("SIGMOID")) {
             activation_function = Activation.SIGMOID;
-        } else if (args[4].matches("TANH")) {
+        } else if (args[3].matches("TANH")) {
             activation_function = Activation.TANH;
         } else {
             throw new IllegalArgumentException(
                     "Not correct activation function");
         }
 
-        for (int neuron_number = begin_neuron_number;
-             neuron_number <= neuron_number_end; neuron_number++) {
-            double average_auc = 0;
-            System.out.println("Neuron number : " + neuron_number);
-            HyperParameters parameters
-                    = new HyperParameters(neuron_number, learning_rate,
-                    optimization_algorithm, activation_function);
-            NeuralNetwork nn = new NeuralNetwork(parameters);
+        double average_auc = 0;
+        double average_aucpr = 0;
+        System.out.println("Neuron number : " + neuron_number);
+        Logger logger = LoggerFactory.getLogger(MainDL4J.class);
+        HyperParameters parameters
+                = new HyperParameters(logger, neuron_number, learning_rate,
+                optimization_algorithm, activation_function,
+                batch_size, epoch_number);
+        NeuralNetwork nn = new NeuralNetwork(parameters);
 
-            HashMap<MultiLayerNetwork, Double> map = nn.runKFold(
-                    data_file,
-                    expected_file,
-                    fold_number,
-                    increase_ratio);
-            for (Double d : map.values()) {
-                average_auc = average_auc + d;
-            }
-            try (OutputStreamWriter writer = new OutputStreamWriter(
-                    new FileOutputStream("Synthesis_average_AUC.txt", true),
-                    StandardCharsets.UTF_8)) {
-                writer.write("Neuron number : " + neuron_number
-                        + " Learning rate : " + learning_rate
-                        + " Algorithm : " + args[3]
-                        + " Activation function : " + args[4]
-                        + " Average AUC = "
-                        + (average_auc / fold_number) + "\n");
-            } catch (IOException e) {
-                e.printStackTrace();
-            }
+        HashMap<MultiLayerNetwork, double[]> map = nn.runKFold(
+                data_file,
+                expected_file,
+                fold_number,
+                increase_ratio);
+        for (double[] d : map.values()) {
+            average_auc = average_auc + d[0];
+            average_aucpr = average_aucpr + d[1];
+        }
+        try (OutputStreamWriter writer = new OutputStreamWriter(
+                new FileOutputStream("Synthesis_average_AUC.txt", true),
+                StandardCharsets.UTF_8)) {
+            writer.write("Neuron number : " + neuron_number
+                    + " | Learning rate : " + learning_rate
+                    + " | Algorithm : " + optimization_algorithm
+                    + " | Activation function : " + activation_function
+                    + " | Batch Size : " + batch_size
+                    + " |Epoch number : " + epoch_number
+                    + " | Average AUC = "
+                    + (average_auc / fold_number)
+                    + " | Average AUC - PR = "
+                    + (average_aucpr / fold_number) + "\n");
+        } catch (IOException e) {
+            e.printStackTrace();
         }
+    }
 
 
-    }
 }
+
diff --git a/src/main/java/be/cylab/java/wowa/training/MainTest.java b/src/main/java/be/cylab/java/wowa/training/MainTest.java
index 4b940c4c17f78be9c0211c2b2e65652d938989e5..c002f3f6400fda0f7aae2d982e3bf7912e5b0694 100644
--- a/src/main/java/be/cylab/java/wowa/training/MainTest.java
+++ b/src/main/java/be/cylab/java/wowa/training/MainTest.java
@@ -3,6 +3,8 @@ package be.cylab.java.wowa.training;
 import org.deeplearning4j.nn.api.OptimizationAlgorithm;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.nd4j.linalg.activations.Activation;
+import org.slf4j.Logger;
+import org.slf4j.LoggerFactory;
 
 import java.util.HashMap;
 import java.util.List;
@@ -50,11 +52,17 @@ public final class MainTest {
         double learning_rate = 0.01;
         OptimizationAlgorithm algo = OptimizationAlgorithm.CONJUGATE_GRADIENT;
         Activation activation_function = Activation.TANH;
+        int batch_size = 32;
+        int epoch_number = 10;
+        Logger logger = LoggerFactory.getLogger(NeuralNetwork.class);
         HyperParameters nn_parameters = new HyperParameters(
+                logger,
                 neurons_number,
                 learning_rate,
                 algo,
-                activation_function
+                activation_function,
+                batch_size,
+                epoch_number
         );
         NeuralNetwork nn = new NeuralNetwork(nn_parameters);
 
@@ -63,29 +71,29 @@ public final class MainTest {
 
         long start_time = System.currentTimeMillis();
         System.out.println("Wowa training");
-        HashMap<AbstractSolution, Double> map_wt = trainer.runKFold(folds, 2);
+        HashMap<AbstractSolution, double[]> map_wt = trainer.runKFold(folds, 2);
         long end_time = System.currentTimeMillis();
         System.out.println("Execution time : " + (end_time - start_time) / 1000
                 + " seconds");
 
         start_time = System.currentTimeMillis();
         System.out.println("Neural Network learning");
-        HashMap<MultiLayerNetwork, Double> map_nn = nn.runKFold(folds, 10);
+        HashMap<MultiLayerNetwork, double[]> map_nn = nn.runKFold(folds, 10, 5);
         end_time = System.currentTimeMillis();
         System.out.println("Execution time : " + (end_time - start_time) / 1000
                 + " seconds");
 
         double nn_score = 0.0;
         double wt_score = 0.0;
-        for (Double d : map_nn.values()) {
-            nn_score = nn_score + d;
+        for (double[] d : map_nn.values()) {
+            nn_score = nn_score + d[0];
         }
 
         System.out.println("Average AUC for Neural Network learning : "
                 + nn_score / 10);
 
-        for (Double d : map_wt.values()) {
-            wt_score = wt_score + d;
+        for (double[] d : map_wt.values()) {
+            wt_score = wt_score + d[0];
         }
 
         System.out.println("Average AUC for WOWA learning : " + wt_score / 10);
diff --git a/src/main/java/be/cylab/java/wowa/training/NeuralNetwork.java b/src/main/java/be/cylab/java/wowa/training/NeuralNetwork.java
index 35d6a1068444aa9f0321fb31029531690b6daeb5..21270ab0d7850e1e36037fcd680f85bd0b75b9f9 100644
--- a/src/main/java/be/cylab/java/wowa/training/NeuralNetwork.java
+++ b/src/main/java/be/cylab/java/wowa/training/NeuralNetwork.java
@@ -3,29 +3,32 @@ package be.cylab.java.wowa.training;
 import org.datavec.api.records.reader.RecordReader;
 import org.datavec.api.records.reader.impl.csv.CSVRecordReader;
 import org.datavec.api.split.FileSplit;
-import org.datavec.api.util.ClassPathResource;
 import org.deeplearning4j.datasets.datavec.RecordReaderDataSetIterator;
-import org.deeplearning4j.eval.Evaluation;
-import org.deeplearning4j.eval.ROC;
+import org.deeplearning4j.nn.conf.BackpropType;
 import org.deeplearning4j.nn.conf.MultiLayerConfiguration;
 import org.deeplearning4j.nn.conf.NeuralNetConfiguration;
 import org.deeplearning4j.nn.conf.layers.DenseLayer;
 import org.deeplearning4j.nn.conf.layers.OutputLayer;
 import org.deeplearning4j.nn.multilayer.MultiLayerNetwork;
 import org.deeplearning4j.nn.weights.WeightInit;
+import org.nd4j.evaluation.classification.Evaluation;
+import org.nd4j.evaluation.classification.ROC;
+import org.nd4j.evaluation.curves.PrecisionRecallCurve;
+import org.nd4j.evaluation.curves.RocCurve;
 import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.DataSet;
-import org.nd4j.linalg.dataset.SplitTestAndTrain;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
-import org.nd4j.linalg.dataset.api.preprocessor.DataNormalization;
-import org.nd4j.linalg.dataset.api.preprocessor.NormalizerStandardize;
+import org.nd4j.linalg.io.ClassPathResource;
+import org.nd4j.linalg.learning.config.Adam;
 import org.nd4j.linalg.lossfunctions.LossFunctions;
 
 import java.io.IOException;
 import java.util.HashMap;
 import java.util.List;
 
+
+
 /**
  * Class to test neural network learning.
  */
@@ -60,36 +63,41 @@ public final class NeuralNetwork {
      * @param learning
      * @return
      */
-    MultiLayerNetwork learning(
-            final DataSet learning) {
+    MultiLayerNetwork learning(final DataSetIterator learning) {
 
         int neurons_number = parameters.getNeuronsNumber();
         MultiLayerConfiguration configuration
                 = new NeuralNetConfiguration.Builder()
-                .iterations(1000)
+                //               .seed(123)
                 .activation(parameters.getActivationFunction())
                 .optimizationAlgo(
                         parameters.getAlgorithm())
                 .weightInit(WeightInit.XAVIER)
-                .learningRate(parameters.getLearningRate())
-                .regularization(true).l2(0.0001)
+                .updater(new Adam(parameters.getLearningRate()))
+                .l2(0.0001)
                 .list()
                 .layer(0, new DenseLayer.Builder()
-                        .nIn(learning.getFeatures().columns())
+                        .nIn(learning.inputColumns())
                         .nOut(neurons_number).build())
                 .layer(1, new DenseLayer.Builder().nIn(neurons_number)
                         .nOut(neurons_number).build())
                 .layer(2, new OutputLayer.Builder(LossFunctions
-                        .LossFunction.NEGATIVELOGLIKELIHOOD)
+                        .LossFunction.RECONSTRUCTION_CROSSENTROPY)
                         .activation(Activation.SOFTMAX)
                         .nIn(neurons_number)
-                        .nOut(learning.getLabels().columns()).build())
-                .backprop(true).pretrain(false)
+                        .nOut(learning.totalOutcomes()).build())
+                .backpropType(BackpropType.Standard)
                 .build();
 
         MultiLayerNetwork model = new MultiLayerNetwork(configuration);
         model.init();
-        model.fit(learning);
+        //model.setListeners(new ScoreIterationListener(1000));
+        long start_time = System.currentTimeMillis();
+        model.pretrain(learning);
+        model.fit(learning, parameters.getEpochNumber());
+        long end_time = System.currentTimeMillis();
+        System.out.println(": execution time : "
+                + ((end_time - start_time) / 1000));
 
         return model;
     }
@@ -103,7 +111,9 @@ public final class NeuralNetwork {
             final List<List<Double>> data,
             final List<Double> expected) {
 
-        DataSet all_data = Utils.createDataSet(data, expected);
+        DataSetIterator all_data = Utils.createDataSetIterator(data,
+                expected,
+                parameters.getBatchSize());
         //DataSet training_data
         //        = prepareDataSetForTrainingAndTesting(all_data).getTrain();
         return this.learning(all_data);
@@ -135,11 +145,12 @@ public final class NeuralNetwork {
                     new ClassPathResource(filename).getFile()
             ));
             DataSetIterator iterator = new RecordReaderDataSetIterator(
-                    record_reader, 12468, FEATURES_COUNT, CLASSES_COUNT);
-            DataSet all_data = iterator.next();
+                    record_reader, parameters.getBatchSize(),
+                    FEATURES_COUNT,
+                    CLASSES_COUNT);
             //DataSet training_data
             //       = prepareDataSetForTrainingAndTesting(all_data).getTrain();
-            return learning(all_data);
+            return learning(iterator);
 
         } catch (IOException e) {
             e.printStackTrace();
@@ -155,19 +166,26 @@ public final class NeuralNetwork {
      * @param testing
      * @param network
      */
-    public Double modelEvaluation(
+    public double[] modelEvaluation(
             final DataSet testing,
-            final MultiLayerNetwork network) {
+            final MultiLayerNetwork network,
+            final int fold_number) {
         INDArray output
-                = network.output((testing.getFeatureMatrix()));
+                = network.output((testing.getFeatures()));
 
         Evaluation eval = new Evaluation(testing.getLabels().columns());
         eval.eval(testing.getLabels(), output);
         System.out.println(eval.stats());
-        ROC roc = new ROC(testing.getLabels().columns());
+        ROC roc = new ROC(0);
         roc.eval(testing.getLabels(), output);
+        RocCurve roc_curve = roc.getRocCurve();
+        Utils.generateRocCurve(roc_curve, this.parameters, fold_number);
+        PrecisionRecallCurve pr = roc.getPrecisionRecallCurve();
+        Utils.generatePRCurve(pr, this.parameters, fold_number);
         System.out.println(roc.stats());
-        return roc.calculateAUC();
+        double[] solution = new double[] {roc.calculateAUC(),
+                roc.calculateAUCPR()};
+        return solution;
     }
 
     /**
@@ -175,25 +193,13 @@ public final class NeuralNetwork {
      * @param network
      * @return
      */
-    public Double modelEvaluation(
+    public double[] modelEvaluation(
             final TrainingDataset testing,
-            final MultiLayerNetwork network) {
+            final MultiLayerNetwork network,
+            final int fold_number) {
         DataSet test
                 = Utils.createDataSet(testing.getData(), testing.getExpected());
-        return modelEvaluation(test, network);
-    }
-
-    /**
-     * @param data
-     * @return
-     */
-    SplitTestAndTrain prepareDataSetForTrainingAndTesting(final DataSet data) {
-        data.shuffle();
-        DataNormalization normalizer = new NormalizerStandardize();
-        normalizer.fit(data);
-        normalizer.transform(data);
-
-        return data.splitTestAndTrain(parameters.getPercentTestTrain());
+        return modelEvaluation(test, network, fold_number);
     }
 
     /**
@@ -203,14 +209,14 @@ public final class NeuralNetwork {
      * @param increase_ratio
      * @return
      */
-    public HashMap<MultiLayerNetwork, Double> runKFold(
+    public HashMap<MultiLayerNetwork, double[]> runKFold(
             final List<List<Double>> data,
             final List<Double> expected,
             final int fold_number,
             final int increase_ratio) {
         TrainingDataset dataset = new TrainingDataset(data, expected);
         List<TrainingDataset> folds = dataset.prepareFolds(fold_number);
-        HashMap<MultiLayerNetwork, Double> map = new HashMap<>();
+        HashMap<MultiLayerNetwork, double[]> map = new HashMap<>();
         for (int i = 0; i < fold_number; i++) {
             TrainingDataset testingfold = folds.get(i);
             TrainingDataset learning_fold = new TrainingDataset();
@@ -224,7 +230,7 @@ public final class NeuralNetwork {
             MultiLayerNetwork nn = run(
                     dataset_increased.getData(),
                     dataset_increased.getExpected());
-            Double score = modelEvaluation(testingfold, nn);
+            double[] score = modelEvaluation(testingfold, nn, i);
 
             map.put(nn, score);
         }
@@ -238,7 +244,7 @@ public final class NeuralNetwork {
      * @param increase_ratio
      * @return
      */
-    public HashMap<MultiLayerNetwork, Double> runKFold(
+    public HashMap<MultiLayerNetwork, double[]> runKFold(
             final String filename_data,
             final String filename_expected,
             final int fold_number,
@@ -251,10 +257,18 @@ public final class NeuralNetwork {
         return runKFold(data, expected, fold_number, increase_ratio);
     }
 
-    HashMap<MultiLayerNetwork, Double> runKFold(
+    /**
+     * RunKFold method created to use the same dataset for WT and NN.
+     *
+     * @param prepared_folds
+     * @param increase_ratio
+     * @return
+     */
+    HashMap<MultiLayerNetwork, double[]> runKFold(
             final List<TrainingDataset> prepared_folds,
-            final int increase_ratio) {
-        HashMap<MultiLayerNetwork, Double> map = new HashMap<>();
+            final int increase_ratio,
+            final int nbr_epoch) {
+        HashMap<MultiLayerNetwork, double[]> map = new HashMap<>();
         for (int i = 0; i < prepared_folds.size(); i++) {
             TrainingDataset testingfold = prepared_folds.get(i);
             TrainingDataset learning_fold = new TrainingDataset();
@@ -269,7 +283,7 @@ public final class NeuralNetwork {
             MultiLayerNetwork nn = run(
                     dataset_increased.getData(),
                     dataset_increased.getExpected());
-            Double score = modelEvaluation(testingfold, nn);
+            double[] score = modelEvaluation(testingfold, nn, i);
             map.put(nn, score);
         }
         return map;
diff --git a/src/main/java/be/cylab/java/wowa/training/SolutionAUC.java b/src/main/java/be/cylab/java/wowa/training/SolutionAUC.java
index 551ae7a29731b53c20c181618de22e06a9d11854..d220f04325f3cc37ece3330834b45e3d9381424a 100644
--- a/src/main/java/be/cylab/java/wowa/training/SolutionAUC.java
+++ b/src/main/java/be/cylab/java/wowa/training/SolutionAUC.java
@@ -23,6 +23,6 @@ public class SolutionAUC extends AbstractSolution {
             final List<List<Double>> data,
             final List<Double> expected) {
         this.fitness_score = 0.0;
-        this.fitness_score = -(super.computeAUC(data, expected));
+        this.fitness_score = -(super.computeAUC(data, expected, null));
     }
 }
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 87c45ff5245a0a732f92122a9b087ccf8437a575..3a882f7a5c71382a1ebdac36f4fbc47a0e4cc07e 100644
--- a/src/main/java/be/cylab/java/wowa/training/Trainer.java
+++ b/src/main/java/be/cylab/java/wowa/training/Trainer.java
@@ -74,8 +74,7 @@ public class Trainer {
 
             //Log best element of the generation
             if (this.getParameters().getLogger() != null) {
-                this.getParameters().getLogger().log(
-                        Level.INFO,
+                this.getParameters().getLogger().info(
                         "Generation " + (generation + 1) + " : "
                                 + best_solution.toString());
             }
@@ -98,14 +97,14 @@ public class Trainer {
      * @param fold_number
      * @return
      */
-    public final HashMap<AbstractSolution, Double> runKFold(
+    public final HashMap<AbstractSolution, double[]> runKFold(
             final List<List<Double>> data,
             final List<Double> expected,
             final int fold_number,
             final int increase_ration_alert) {
         TrainingDataset dataset = new TrainingDataset(data, expected);
         List<TrainingDataset> folds = dataset.prepareFolds(fold_number);
-        HashMap<AbstractSolution, Double> map = new HashMap<>();
+        HashMap<AbstractSolution, double[]> map = new HashMap<>();
         for (int i = 0; i < fold_number; i++) {
             TrainingDataset testing = folds.get(i);
             TrainingDataset learning = new TrainingDataset();
@@ -119,9 +118,14 @@ public class Trainer {
             AbstractSolution sol = run(
                     dataset_increased.getData(),
                     dataset_increased.getExpected());
-            Double score = sol.computeAUC(
+            Double auc = sol.computeAUC(
                     testing.getData(),
-                    testing.getExpected());
+                    testing.getExpected(),
+                    "RocCurve" + (i + 1) + ".png");
+            Double prauc = sol.computePRAUC(
+                    testing.getData(),
+                    testing.getExpected(),
+                    "PRAUC" + (i + 1) + ".png");
 
             Utils.computeStatisticalInformationAndStoreInTextFile(
                     testing.getData(),
@@ -129,9 +133,10 @@ public class Trainer {
                     100,
                     "Fold_" + (i + 1) + ".txt",
                     sol);
-            map.put(sol, score);
-            parameters.getLogger().log(Level.WARNING,
-                    "Solution fold " + i + ":" + sol + " - AUC: " + score);
+            double[] out = new double[]{auc, prauc};
+            map.put(sol, out);
+            parameters.getLogger().log(Level.INFO,
+                    "Solution fold " + i + ":" + sol + " - AUC: " + auc);
         }
         return map;
 
@@ -145,7 +150,7 @@ public class Trainer {
      * @param fold_number
      * @return
      */
-    public final HashMap<AbstractSolution, Double> runKFold(
+    public final HashMap<AbstractSolution, double[]> runKFold(
             final String data_file_name,
             final String expected_file_name,
             final int fold_number,
@@ -162,10 +167,10 @@ public class Trainer {
      * @param increase_ratio
      * @return
      */
-    final HashMap<AbstractSolution, Double> runKFold(
+    final HashMap<AbstractSolution, double[]> runKFold(
             final List<TrainingDataset> prepared_folds,
             final int increase_ratio) {
-        HashMap<AbstractSolution, Double> map = new HashMap<>();
+        HashMap<AbstractSolution, double[]> map = new HashMap<>();
         for (int i = 0; i < prepared_folds.size(); i++) {
             TrainingDataset testing = prepared_folds.get(i);
             TrainingDataset learning = new TrainingDataset();
@@ -179,12 +184,20 @@ public class Trainer {
             AbstractSolution sol = run(
                     dataset_increased.getData(),
                     dataset_increased.getExpected());
-            Double score = sol.computeAUC(
+            Double auc = sol.computeAUC(
+                    testing.getData(),
+                    testing.getExpected(),
+                    "RocCurve" + (i + 1) + ".png");
+
+            Double prauc = sol.computePRAUC(
                     testing.getData(),
-                    testing.getExpected());
-            System.out.println("Fold number : " + (i + 1) + "AUC : " + score);
+                    testing.getExpected(),
+                    "PRAUC" + (i + 1) + ".png");
+
+            double[] out = new double[]{auc, prauc};
+            System.out.println("Fold number : " + (i + 1) + "AUC : " + auc);
 
-            map.put(sol, score);
+            map.put(sol, out);
         }
         return map;
     }
diff --git a/src/main/java/be/cylab/java/wowa/training/TrainerParameters.java b/src/main/java/be/cylab/java/wowa/training/TrainerParameters.java
index b70de00cf35a51c9fa5de4f3d146285759c7ce41..6592b2df65e3d63329a67c3c022a95e8e595ba31 100644
--- a/src/main/java/be/cylab/java/wowa/training/TrainerParameters.java
+++ b/src/main/java/be/cylab/java/wowa/training/TrainerParameters.java
@@ -1,5 +1,6 @@
 package be.cylab.java.wowa.training;
 
+
 import java.util.logging.Logger;
 
 /**
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 1122429512d9b1f67bda303865fc81b9fed5e466..2fa2915cfe770f61f9ca53a3d5743b97ef9ab1a9 100644
--- a/src/main/java/be/cylab/java/wowa/training/Utils.java
+++ b/src/main/java/be/cylab/java/wowa/training/Utils.java
@@ -5,6 +5,16 @@ import com.owlike.genson.Genson;
 import info.debatty.java.aggregation.WOWA;
 import org.apache.commons.lang3.ArrayUtils;
 import org.deeplearning4j.datasets.iterator.DoublesDataSetIterator;
+import org.deeplearning4j.nn.api.OptimizationAlgorithm;
+import org.knowm.xchart.BitmapEncoder;
+import org.knowm.xchart.XYChart;
+import org.knowm.xchart.XYChartBuilder;
+import org.knowm.xchart.XYSeries;
+import org.knowm.xchart.style.lines.SeriesLines;
+import org.knowm.xchart.style.markers.SeriesMarkers;
+import org.nd4j.evaluation.curves.PrecisionRecallCurve;
+import org.nd4j.evaluation.curves.RocCurve;
+import org.nd4j.linalg.activations.Activation;
 import org.nd4j.linalg.api.ndarray.INDArray;
 import org.nd4j.linalg.dataset.DataSet;
 import org.nd4j.linalg.dataset.api.iterator.DataSetIterator;
@@ -185,28 +195,21 @@ final class Utils {
      * @param expected
      * @return
      */
-    static boolean[] convertExpectedToBooleanArrayTrueAlert(
+    static double[] convertExpectedToBooleanArrayTrueAlert(
             final List<Double> expected) {
-        boolean[] true_alert = new boolean[expected.size()];
+        double[] true_alert = new double[expected.size()];
         for (int i = 0; i < expected.size(); i++) {
-            if (expected.get(i) == 1) {
-                true_alert[i] = true;
-            } else if (expected.get(i) == 0) {
-                true_alert[i] = false;
-            } else {
-                throw new IllegalStateException(
-                        "Expected must be equal to 0 or 1 to compute ROC");
-            }
+            true_alert[i] = expected.get(i);
         }
         return true_alert;
     }
 
-    /**
-     * Function to convert a List of doubles to an Array of doubles.
-     *
-     * @param elements
-     * @return
-     */
+            /**
+             * Function to convert a List of doubles to an Array of doubles.
+             *
+             * @param elements
+             * @return
+             */
     static double[] convertListDoubleToArrayDouble(
             final List<Double> elements) {
         Double[] w = new Double[elements.size()];
@@ -583,7 +586,7 @@ final class Utils {
         double[][] data_array = new double[data.size()][data.get(0).size()];
         double[][] expected_array = new double[expected.size()][2];
         for (int i = 0; i < data.size(); i++) {
-            if (expected.get(i) == 1.0) {
+            if (expected.get(i) == 0.0) {
                 expected_array[i][0] = 1.0;
                 expected_array[i][1] = 0.0;
             } else {
@@ -604,7 +607,7 @@ final class Utils {
             final List<List<Double>> data,
             final List<Double> expected,
             final int batch_size
-            ) {
+    ) {
 
         List<Pair<double[], double[]>> ds = new ArrayList<>();
         if (data.size() != expected.size()) {
@@ -612,7 +615,7 @@ final class Utils {
                     "Data and Expected must have the same size");
         }
         for (int i = 0; i < data.size(); i++) {
-            if (expected.get(i) == 1.0) {
+            if (expected.get(i) == 0.0) {
                 double[] d = convertListDoubleToArrayDouble(data.get(i));
                 double[] e = {1.0, 0.0};
                 Pair<double[], double[]> p
@@ -631,4 +634,91 @@ final class Utils {
         DataSetIterator dsi = new DoublesDataSetIterator(ds, batch_size);
         return dsi;
     }
+
+    static void generateRocCurve(final RocCurve roc,
+                                 final HyperParameters param,
+                                 final int fold_number) {
+        String title = "Roc curve";
+        String x_axis = "False Alarm";
+        String y_axis = "True detection";
+        String filename
+                = Utils.generateFilenameFromHyperParameters(param,
+                fold_number,
+                true);
+
+        XYChart chart = new XYChartBuilder().width(600).height(400)
+                .title(title).xAxisTitle(x_axis).yAxisTitle(y_axis)
+                .build();
+
+        double[] dotted_x = new double[]{0, 1};
+        double[] dotted_y = new double[]{0, 1};
+        XYSeries dotted = chart.addSeries("Random Classification",
+                dotted_x, dotted_y);
+        dotted.setLineStyle(SeriesLines.DASH_DASH);
+        dotted.setMarker(SeriesMarkers.NONE);
+
+        XYSeries pr = chart.addSeries("ROC curve", roc.getX(), roc.getY());
+        pr.setMarker(SeriesMarkers.NONE);
+        try {
+            BitmapEncoder.saveBitmapWithDPI(chart,
+                    filename,
+                    BitmapEncoder.BitmapFormat.PNG,
+                    300);
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+    }
+
+    static void generatePRCurve(final PrecisionRecallCurve pr,
+                                final HyperParameters param,
+                                final int fold_number) {
+        String title = "Precision-Recall curve";
+        String x_axis = "Recall";
+        String y_axis = "Precision";
+        String filename
+                = Utils.generateFilenameFromHyperParameters(param,
+                fold_number,
+                false);
+
+        XYChart chart = new XYChartBuilder().width(600).height(400)
+                .title(title).xAxisTitle(x_axis).yAxisTitle(y_axis)
+                .build();
+
+        XYSeries series = chart.addSeries("P-R curve", pr.getX(), pr.getY());
+        series.setMarker(SeriesMarkers.NONE);
+        try {
+            BitmapEncoder.saveBitmapWithDPI(chart,
+                    filename,
+                    BitmapEncoder.BitmapFormat.PNG,
+                    300);
+        } catch (IOException e) {
+            e.printStackTrace();
+        }
+    }
+
+    static String generateFilenameFromHyperParameters(
+            final HyperParameters param,
+            final int fold_number,
+            final boolean roc) {
+        double learning_rate = param.getLearningRate();
+        int neurons_number = param.getNeuronsNumber();
+        int batch_size = param.getBatchSize();
+        OptimizationAlgorithm algo = param.getAlgorithm();
+        Activation activation = param.getActivationFunction();
+        int epoch_number = param.getEpochNumber();
+        String curve_type;
+        if (roc) {
+            curve_type = "ROC Curve";
+        } else {
+            curve_type = "P-R Curve";
+        }
+        String filename = curve_type + " Neuron number : " + neurons_number
+                + " - Learning Rate : " + learning_rate
+                + " - Batch Size : " + batch_size
+                + " - Epoch number : " + epoch_number
+                + " - Algorithm : " + algo.toString()
+                + " - Activation function : " + activation.toString()
+                + " - Fold number : " + fold_number;
+        return filename;
+    }
 }
diff --git a/src/main/resources/new_dataset_aggregated_after_learning_pop110_cross60_mut15_gen150_random_rws.csv b/src/main/resources/new_dataset_aggregated_after_learning_pop110_cross60_mut15_gen150_random_rws.csv
new file mode 100644
index 0000000000000000000000000000000000000000..94024b7c70958314f19a1a67e3a47ce8f171a4d4
--- /dev/null
+++ b/src/main/resources/new_dataset_aggregated_after_learning_pop110_cross60_mut15_gen150_random_rws.csv
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diff --git a/src/test/java/be/cylab/java/wowa/training/AbstractSolutionTest.java b/src/test/java/be/cylab/java/wowa/training/AbstractSolutionTest.java
index 27d1abb5bc1d4d446cfaab7f703961ebc8c5d402..ea3eb96cb681b570adf3d1dce95ea4986e2c4b86 100644
--- a/src/test/java/be/cylab/java/wowa/training/AbstractSolutionTest.java
+++ b/src/test/java/be/cylab/java/wowa/training/AbstractSolutionTest.java
@@ -1,6 +1,6 @@
 package be.cylab.java.wowa.training;
 
-import be.cylab.java.roc.RocCoordinates;
+import be.cylab.java.roc.CurveCoordinates;
 import org.junit.jupiter.api.Test;
 
 import java.util.ArrayList;
@@ -35,22 +35,22 @@ class AbstractSolutionTest {
         SolutionDistance solution = new SolutionDistance(5, 1234);
         List<List<Double>> data = generateData(20, 5);
         List<Double> expected = generateExpectedForRoc(20);
-        List<RocCoordinates> coord = solution.computeRocPoints(data, expected,false);
-        List<RocCoordinates> roc_assert = generateRocCoordinatesAssertion();
+        List<CurveCoordinates> coord = solution.computeRocPoints(data, expected,false);
+        List<CurveCoordinates> roc_assert = generateRocCoordinatesAssertion();
         for (int i = 0; i < coord.size(); i++) {
-            assertEquals(roc_assert.get(i).getFalseAlarm(), coord.get(i).getFalseAlarm());
-            assertEquals(roc_assert.get(i).getTrueDetection(), coord.get(i).getTrueDetection());
+            assertEquals(roc_assert.get(i).getXAxis(), coord.get(i).getXAxis());
+            assertEquals(roc_assert.get(i).getYAxis(), coord.get(i).getYAxis());
             //System.out.println("roc_assert.add(new RocCoordinates("+coord.get(i).getFalseAlarm() +"," + coord.get(i).getTrueDetection()+"));");
         }
         //Test with SolutionAUC
         SolutionAUC solution2 = new SolutionAUC(5, 1234);
         List<List<Double>> data2 = generateData(20, 5);
         List<Double> expected2 = generateExpectedForRoc(20);
-        List<RocCoordinates> coord2 = solution2.computeRocPoints(data2, expected2,false);
-        List<RocCoordinates> roc_assert2 = generateRocCoordinatesAssertion();
+        List<CurveCoordinates> coord2 = solution2.computeRocPoints(data2, expected2,false);
+        List<CurveCoordinates> roc_assert2 = generateRocCoordinatesAssertion();
         for (int i = 0; i < coord2.size(); i++) {
-            assertEquals(roc_assert2.get(i).getFalseAlarm(), coord2.get(i).getFalseAlarm());
-            assertEquals(roc_assert2.get(i).getTrueDetection(), coord2.get(i).getTrueDetection());
+            assertEquals(roc_assert2.get(i).getXAxis(), coord2.get(i).getXAxis());
+            assertEquals(roc_assert2.get(i).getYAxis(), coord2.get(i).getYAxis());
             //System.out.println("roc_assert.add(new RocCoordinates("+coord.get(i).getFalseAlarm() +"," + coord.get(i).getTrueDetection()+"));");
         }
     }
@@ -60,13 +60,13 @@ class AbstractSolutionTest {
         SolutionDistance solution = new SolutionDistance(5, 1234);
         List<List<Double>> data = generateData(20, 5);
         List<Double> expected = generateExpectedForRoc(20);
-        double auc = solution.computeAUC(data, expected);
+        double auc = solution.computeAUC(data, expected, "Test1.png");
         assertEquals(0.5119047619047619, auc);
 
         SolutionAUC solution2 = new SolutionAUC(5, 1234);
         List<List<Double>> data2 = generateData(20, 5);
         List<Double> expected2 = generateExpectedForRoc(20);
-        double auc2 = solution2.computeAUC(data2, expected2);
+        double auc2 = solution2.computeAUC(data2, expected2, "Test2.png");
         assertEquals(0.5119047619047619, auc2);
     }
 
@@ -121,29 +121,29 @@ class AbstractSolutionTest {
         return  expected;
     }
 
-    private  static List<RocCoordinates> generateRocCoordinatesAssertion() {
-        List<RocCoordinates> roc_assert = new ArrayList<>();
-        roc_assert.add(new RocCoordinates(0.0,0.0));
-        roc_assert.add(new RocCoordinates(0.0,0.07142857142857142));
-        roc_assert.add(new RocCoordinates(0.16666666666666666,0.07142857142857142));
-        roc_assert.add(new RocCoordinates(0.16666666666666666,0.14285714285714285));
-        roc_assert.add(new RocCoordinates(0.16666666666666666,0.21428571428571427));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.21428571428571427));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.2857142857142857));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.35714285714285715));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.42857142857142855));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.5));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.5714285714285714));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.6428571428571429));
-        roc_assert.add(new RocCoordinates(0.5,0.6428571428571429));
-        roc_assert.add(new RocCoordinates(0.6666666666666666,0.6428571428571429));
-        roc_assert.add(new RocCoordinates(0.8333333333333334,0.6428571428571429));
-        roc_assert.add(new RocCoordinates(0.8333333333333334,0.7142857142857143));
-        roc_assert.add(new RocCoordinates(0.8333333333333334,0.7857142857142857));
-        roc_assert.add(new RocCoordinates(0.8333333333333334,0.8571428571428571));
-        roc_assert.add(new RocCoordinates(1.0,0.8571428571428571));
-        roc_assert.add(new RocCoordinates(1.0,0.9285714285714286));
-        roc_assert.add(new RocCoordinates(1.0,1.0));
+    private  static List<CurveCoordinates> generateRocCoordinatesAssertion() {
+        List<CurveCoordinates> roc_assert = new ArrayList<>();
+        roc_assert.add(new CurveCoordinates(0.0,0.0));
+        roc_assert.add(new CurveCoordinates(0.0,0.07142857142857142));
+        roc_assert.add(new CurveCoordinates(0.16666666666666666,0.07142857142857142));
+        roc_assert.add(new CurveCoordinates(0.16666666666666666,0.14285714285714285));
+        roc_assert.add(new CurveCoordinates(0.16666666666666666,0.21428571428571427));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.21428571428571427));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.2857142857142857));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.35714285714285715));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.42857142857142855));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.5));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.5714285714285714));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.6428571428571429));
+        roc_assert.add(new CurveCoordinates(0.5,0.6428571428571429));
+        roc_assert.add(new CurveCoordinates(0.6666666666666666,0.6428571428571429));
+        roc_assert.add(new CurveCoordinates(0.8333333333333334,0.6428571428571429));
+        roc_assert.add(new CurveCoordinates(0.8333333333333334,0.7142857142857143));
+        roc_assert.add(new CurveCoordinates(0.8333333333333334,0.7857142857142857));
+        roc_assert.add(new CurveCoordinates(0.8333333333333334,0.8571428571428571));
+        roc_assert.add(new CurveCoordinates(1.0,0.8571428571428571));
+        roc_assert.add(new CurveCoordinates(1.0,0.9285714285714286));
+        roc_assert.add(new CurveCoordinates(1.0,1.0));
         return roc_assert;
     }
 
diff --git a/src/test/java/be/cylab/java/wowa/training/SolutionAUCTest.java b/src/test/java/be/cylab/java/wowa/training/SolutionAUCTest.java
index d2dd5f0620437517c2886f4d7816b439d303a42a..4f3c28d25e247a1170dfccb27048396af45bc629 100644
--- a/src/test/java/be/cylab/java/wowa/training/SolutionAUCTest.java
+++ b/src/test/java/be/cylab/java/wowa/training/SolutionAUCTest.java
@@ -1,6 +1,6 @@
 package be.cylab.java.wowa.training;
 
-import be.cylab.java.roc.RocCoordinates;
+import be.cylab.java.roc.CurveCoordinates;
 import org.junit.jupiter.api.BeforeEach;
 import org.junit.jupiter.api.Test;
 
@@ -42,11 +42,11 @@ class SolutionAUCTest {
         SolutionAUC solution = new SolutionAUC(5, 1234);
         List<List<Double>> data = generateData(20, 5);
         List<Double> expected = generateExpectedForRoc(20);
-        List<RocCoordinates> coord = solution.computeRocPoints(data, expected,false);
-        List<RocCoordinates> roc_assert = generateRocCoordinatesAssertion();
+        List<CurveCoordinates> coord = solution.computeRocPoints(data, expected,false);
+        List<CurveCoordinates> roc_assert = generateRocCoordinatesAssertion();
         for (int i = 0; i < coord.size(); i++) {
-            assertEquals(roc_assert.get(i).getFalseAlarm(), coord.get(i).getFalseAlarm());
-            assertEquals(roc_assert.get(i).getTrueDetection(), coord.get(i).getTrueDetection());
+            assertEquals(roc_assert.get(i).getXAxis(), coord.get(i).getXAxis());
+            assertEquals(roc_assert.get(i).getYAxis(), coord.get(i).getYAxis());
         }
     }
 
@@ -85,29 +85,29 @@ class SolutionAUCTest {
         return  expected;
     }
 
-    private  static List<RocCoordinates> generateRocCoordinatesAssertion() {
-        List<RocCoordinates> roc_assert = new ArrayList<>();
-        roc_assert.add(new RocCoordinates(0.0,0.0));
-        roc_assert.add(new RocCoordinates(0.0,0.07142857142857142));
-        roc_assert.add(new RocCoordinates(0.16666666666666666,0.07142857142857142));
-        roc_assert.add(new RocCoordinates(0.16666666666666666,0.14285714285714285));
-        roc_assert.add(new RocCoordinates(0.16666666666666666,0.21428571428571427));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.21428571428571427));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.2857142857142857));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.35714285714285715));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.42857142857142855));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.5));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.5714285714285714));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.6428571428571429));
-        roc_assert.add(new RocCoordinates(0.5,0.6428571428571429));
-        roc_assert.add(new RocCoordinates(0.6666666666666666,0.6428571428571429));
-        roc_assert.add(new RocCoordinates(0.8333333333333334,0.6428571428571429));
-        roc_assert.add(new RocCoordinates(0.8333333333333334,0.7142857142857143));
-        roc_assert.add(new RocCoordinates(0.8333333333333334,0.7857142857142857));
-        roc_assert.add(new RocCoordinates(0.8333333333333334,0.8571428571428571));
-        roc_assert.add(new RocCoordinates(1.0,0.8571428571428571));
-        roc_assert.add(new RocCoordinates(1.0,0.9285714285714286));
-        roc_assert.add(new RocCoordinates(1.0,1.0));
+    private  static List<CurveCoordinates> generateRocCoordinatesAssertion() {
+        List<CurveCoordinates> roc_assert = new ArrayList<>();
+        roc_assert.add(new CurveCoordinates(0.0,0.0));
+        roc_assert.add(new CurveCoordinates(0.0,0.07142857142857142));
+        roc_assert.add(new CurveCoordinates(0.16666666666666666,0.07142857142857142));
+        roc_assert.add(new CurveCoordinates(0.16666666666666666,0.14285714285714285));
+        roc_assert.add(new CurveCoordinates(0.16666666666666666,0.21428571428571427));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.21428571428571427));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.2857142857142857));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.35714285714285715));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.42857142857142855));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.5));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.5714285714285714));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.6428571428571429));
+        roc_assert.add(new CurveCoordinates(0.5,0.6428571428571429));
+        roc_assert.add(new CurveCoordinates(0.6666666666666666,0.6428571428571429));
+        roc_assert.add(new CurveCoordinates(0.8333333333333334,0.6428571428571429));
+        roc_assert.add(new CurveCoordinates(0.8333333333333334,0.7142857142857143));
+        roc_assert.add(new CurveCoordinates(0.8333333333333334,0.7857142857142857));
+        roc_assert.add(new CurveCoordinates(0.8333333333333334,0.8571428571428571));
+        roc_assert.add(new CurveCoordinates(1.0,0.8571428571428571));
+        roc_assert.add(new CurveCoordinates(1.0,0.9285714285714286));
+        roc_assert.add(new CurveCoordinates(1.0,1.0));
         return roc_assert;
     }
 }
\ No newline at end of file
diff --git a/src/test/java/be/cylab/java/wowa/training/SolutionDistanceTest.java b/src/test/java/be/cylab/java/wowa/training/SolutionDistanceTest.java
index b6e2061ad2d3a56cb1b5d3fa8f97fbe637e173c8..22566ef971e190ee537451a0eb242c25d0c97aba 100644
--- a/src/test/java/be/cylab/java/wowa/training/SolutionDistanceTest.java
+++ b/src/test/java/be/cylab/java/wowa/training/SolutionDistanceTest.java
@@ -1,6 +1,6 @@
 package be.cylab.java.wowa.training;
 
-import be.cylab.java.roc.RocCoordinates;
+import be.cylab.java.roc.CurveCoordinates;
 import org.junit.jupiter.api.Test;
 
 import java.util.ArrayList;
@@ -36,11 +36,11 @@ class SolutionDistanceTest  {
         SolutionDistance solution = new SolutionDistance(5, 1234);
         List<List<Double>> data = generateData(20, 5);
         List<Double> expected = generateExpectedForRoc(20);
-        List<RocCoordinates> coord = solution.computeRocPoints(data, expected,false);
-        List<RocCoordinates> roc_assert = generateRocCoordinatesAssertion();
+        List<CurveCoordinates> coord = solution.computeRocPoints(data, expected,false);
+        List<CurveCoordinates> roc_assert = generateRocCoordinatesAssertion();
         for (int i = 0; i < coord.size(); i++) {
-            assertEquals(roc_assert.get(i).getFalseAlarm(), coord.get(i).getFalseAlarm());
-            assertEquals(roc_assert.get(i).getTrueDetection(), coord.get(i).getTrueDetection());
+            assertEquals(roc_assert.get(i).getXAxis(), coord.get(i).getXAxis());
+            assertEquals(roc_assert.get(i).getYAxis(), coord.get(i).getYAxis());
         }
     }
 
@@ -80,29 +80,29 @@ class SolutionDistanceTest  {
         return  expected;
     }
 
-    private  static List<RocCoordinates> generateRocCoordinatesAssertion() {
-        List<RocCoordinates> roc_assert = new ArrayList<>();
-        roc_assert.add(new RocCoordinates(0.0,0.0));
-        roc_assert.add(new RocCoordinates(0.0,0.07142857142857142));
-        roc_assert.add(new RocCoordinates(0.16666666666666666,0.07142857142857142));
-        roc_assert.add(new RocCoordinates(0.16666666666666666,0.14285714285714285));
-        roc_assert.add(new RocCoordinates(0.16666666666666666,0.21428571428571427));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.21428571428571427));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.2857142857142857));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.35714285714285715));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.42857142857142855));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.5));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.5714285714285714));
-        roc_assert.add(new RocCoordinates(0.3333333333333333,0.6428571428571429));
-        roc_assert.add(new RocCoordinates(0.5,0.6428571428571429));
-        roc_assert.add(new RocCoordinates(0.6666666666666666,0.6428571428571429));
-        roc_assert.add(new RocCoordinates(0.8333333333333334,0.6428571428571429));
-        roc_assert.add(new RocCoordinates(0.8333333333333334,0.7142857142857143));
-        roc_assert.add(new RocCoordinates(0.8333333333333334,0.7857142857142857));
-        roc_assert.add(new RocCoordinates(0.8333333333333334,0.8571428571428571));
-        roc_assert.add(new RocCoordinates(1.0,0.8571428571428571));
-        roc_assert.add(new RocCoordinates(1.0,0.9285714285714286));
-        roc_assert.add(new RocCoordinates(1.0,1.0));
+    private  static List<CurveCoordinates> generateRocCoordinatesAssertion() {
+        List<CurveCoordinates> roc_assert = new ArrayList<>();
+        roc_assert.add(new CurveCoordinates(0.0,0.0));
+        roc_assert.add(new CurveCoordinates(0.0,0.07142857142857142));
+        roc_assert.add(new CurveCoordinates(0.16666666666666666,0.07142857142857142));
+        roc_assert.add(new CurveCoordinates(0.16666666666666666,0.14285714285714285));
+        roc_assert.add(new CurveCoordinates(0.16666666666666666,0.21428571428571427));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.21428571428571427));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.2857142857142857));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.35714285714285715));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.42857142857142855));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.5));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.5714285714285714));
+        roc_assert.add(new CurveCoordinates(0.3333333333333333,0.6428571428571429));
+        roc_assert.add(new CurveCoordinates(0.5,0.6428571428571429));
+        roc_assert.add(new CurveCoordinates(0.6666666666666666,0.6428571428571429));
+        roc_assert.add(new CurveCoordinates(0.8333333333333334,0.6428571428571429));
+        roc_assert.add(new CurveCoordinates(0.8333333333333334,0.7142857142857143));
+        roc_assert.add(new CurveCoordinates(0.8333333333333334,0.7857142857142857));
+        roc_assert.add(new CurveCoordinates(0.8333333333333334,0.8571428571428571));
+        roc_assert.add(new CurveCoordinates(1.0,0.8571428571428571));
+        roc_assert.add(new CurveCoordinates(1.0,0.9285714285714286));
+        roc_assert.add(new CurveCoordinates(1.0,1.0));
         return roc_assert;
     }
 }
\ No newline at end of file
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 57b7920d97083f6fe536a7590894db2e2b60bb90..b7a102d73c2578802e00806194429e280d4e1df0 100644
--- a/src/test/java/be/cylab/java/wowa/training/UtilsTest.java
+++ b/src/test/java/be/cylab/java/wowa/training/UtilsTest.java
@@ -181,8 +181,8 @@ class UtilsTest {
         test.add(1.0);
         test.add(0.0);
 
-        boolean[] expected = {false, false, true, false, true, true, false};
-        boolean[] test_converted = Utils.convertExpectedToBooleanArrayTrueAlert(test);
+        double[] expected = {0, 0, 1, 0, 1, 1, 0};
+        double[] test_converted = Utils.convertExpectedToBooleanArrayTrueAlert(test);
         assertEquals(expected.length, test_converted.length);
         for (int i = 0; i < expected.length; i++) {
             assertEquals(expected[i], test_converted[i]);
@@ -196,7 +196,6 @@ class UtilsTest {
         testException.add(1.0);
         testException.add(1.0);
         testException.add(0.0);
-        assertThrows(IllegalStateException.class, () -> {Utils.convertExpectedToBooleanArrayTrueAlert(testException);});
     }
 
     @Test