Issue 25 - refractored java files

example/src/main/java/de/example/benchmark/Benchmark.java

@@ -90,7 +90,6 @@ private void run() {
         });
 
 
-        //Sort and print results with numeration begin with best average accuracy
         System.out.println("Classifier performances (sorted by average accuracy):");
         results.entrySet().stream()
                 .map(entry -> {

example/src/main/java/de/example/data/seaborn/SeabornDataProcessor.java

@@ -145,7 +145,7 @@ public double[][] getTestLabels() {
     }
 
     private double[][] featuresOf(List<Penguin> data) {
-        double[][] features = new double[data.size()][4]; // 4 numerische Eigenschaften
+        double[][] features = new double[data.size()][4];
 
         for (int i = 0; i < data.size(); i++) {
             Penguin p = data.get(i);
@@ -159,7 +159,7 @@ private double[][] featuresOf(List<Penguin> data) {
     }
 
     private double[][] labelsOf(List<Penguin> data) {
-        double[][] labels = new double[data.size()][3]; // 3 Pinguinarten
+        double[][] labels = new double[data.size()][3];
 
         for (int i = 0; i < data.size(); i++) {
             Penguin p = data.get(i);

example/src/main/java/de/example/data/seaborn/SeabornProvider.java

@@ -64,7 +64,7 @@ public double[][] getTestLabels() {
     }
 
     private double[][] featuresOf(List<Penguin> data) {
-        double[][] features = new double[data.size()][4]; // 4 numerische Eigenschaften
+        double[][] features = new double[data.size()][4];
 
         for (int i = 0; i < data.size(); i++) {
             Penguin p = data.get(i);
@@ -77,7 +77,7 @@ private double[][] featuresOf(List<Penguin> data) {
         return features;
     }
     private double[][] labelsOf(List<Penguin> data) {
-        double[][] labels = new double[data.size()][3]; // 3 Pinguinarten
+        double[][] labels = new double[data.size()][3];
 
         for (int i = 0; i < data.size(); i++) {
             Penguin p = data.get(i);

example/src/main/java/de/example/decisiontree/DecisionTreeExample.java

@@ -10,19 +10,16 @@ public class DecisionTreeExample {
   private static final boolean NORMALIZE = true;
 
   public static void main(String[] args) {
-    // Get IRIS dataset
+
     var datasetProvider = new IrisProvider(NORMALIZE, SHUFFLE, 0.6);
     datasetProvider.printStatistics();
 
-    //Get Features and Labels
     double[][] features = datasetProvider.getTrainFeatures();
     double[][] labels = datasetProvider.getTrainLabels();
 
-    // Train Decision Tree
     DecisionTree decisionTree = new DecisionTree(8, 2, 1, 4);
     decisionTree.train(features, labels);
 
-    // Evaluate Decision Tree
     double[][] testFeatures = datasetProvider.getTestFeatures();
     double[][] testLabels = datasetProvider.getTestLabels();
     decisionTree.evaluate(testFeatures, testLabels);

example/src/main/java/de/example/knn/KnnIrisExample.java

@@ -27,7 +27,7 @@ public static void main(String[] args) {
         irisDataProcessor.split(data, TRAIN_TEST_SPLIT_RATIO);
 
         Classifier knn = new KnnClassifier(2);
-        //Train and evaluate
+
         knn.train(irisDataProcessor.getTrainFeatures(), irisDataProcessor.getTrainLabels());
         knn.evaluate(irisDataProcessor.getTestFeatures(), irisDataProcessor.getTestLabels());
     }

example/src/main/java/de/example/knn/KnnSeabornExample.java

@@ -22,15 +22,14 @@ public class KnnSeabornExample {
     private static final File CSV_FILE = new File("example" + File.separator + "datasets" + File.separator + "seaborn-penguins" + File.separator + "penguins.csv");
 
     public static void main(String[] args) {
-        //Load Data, shuffle, normalize, filter incomplete records out.
         var seabornDataProcessor = new SeabornDataProcessor();
         List<Penguin> data = seabornDataProcessor.loadDataSetFromCSV(CSV_FILE, ',', SHUFFLE, NORMALIZE, FILTER_INCOMPLETE_RECORDS);
-        //Split dataset into train and test
+
         Dataset<Penguin> dataset = seabornDataProcessor.split(data, TRAIN_TEST_SPLIT_RATIO);
         var seabornProvider = new SeabornProvider(data, dataset.trainData(), dataset.testData());
         seabornProvider.printStatistics();
+
         Classifier knn = new KnnClassifier(2);
-        //Train and evaluate
         knn.train(seabornProvider.getTrainFeatures(), seabornProvider.getTrainLabels());
         knn.evaluate(seabornProvider.getTestFeatures(), seabornProvider.getTestLabels());
     }

example/src/main/java/de/example/nn/MultilayerPerceptronExample.java

@@ -15,15 +15,12 @@ public class MultilayerPerceptronExample {
     private final static boolean NORMALIZE = true;
 
     public static void main(String[] args) {
-        // Get IRIS dataset
         var datasetProvider = new IrisProvider(NORMALIZE, SHUFFLE, 0.7);
         datasetProvider.printStatistics();
 
-        //Get Features and Labels
         double[][] features = datasetProvider.getTrainFeatures();
         double[][] labels = datasetProvider.getTrainLabels();
 
-        //Get Test Features and Labels
         double[][] testFeatures = datasetProvider.getTestFeatures();
         double[][] testLabels = datasetProvider.getTestLabels();
 

example/src/main/java/de/example/svm/SVMExample.java

@@ -14,14 +14,11 @@ public static void main(String[] args){
         var datasetProvider = new IrisProvider(NORMALIZE, SHUFFLE, 0.6);
         datasetProvider.printStatistics();
 
-        //Get Features and Labels
         var features = datasetProvider.getTrainFeatures();
-        // 1 - SATOSA 2 - VERSICOLOR 3 - VIRGINICA
         var labels = datasetProvider.getTrainLabels();
 
-
         Classifier supportVectorMachine = new SupportVectorMachine(SVMKernel.LINEAR, 1);
-        //ONEvsONE Strategy
+
         supportVectorMachine.train(features, labels);
 
         double[][] testFeatures = datasetProvider.getTestFeatures();

lib/src/main/java/de/edux/data/reader/CSVIDataReader.java

@@ -14,12 +14,12 @@
 public class CSVIDataReader implements IDataReader {
 
     public List<String[]> readFile(File file, char separator) {
-        CSVParser csvParser = new CSVParserBuilder().withSeparator(separator).build(); // custom separator
+        CSVParser customCSVParser = new CSVParserBuilder().withSeparator(separator).build();
         List<String[]> result;
         try(CSVReader reader = new CSVReaderBuilder(
                 new FileReader(file))
-                .withCSVParser(csvParser)   // custom CSV parser
-                .withSkipLines(1)           // skip the first line, header info
+                .withCSVParser(customCSVParser)
+                .withSkipLines(1)
                 .build()){
             result = reader.readAll();
         } catch (CsvException | IOException e) {

lib/src/main/java/de/edux/functions/initialization/Initialization.java

@@ -1,7 +1,6 @@
 package de.edux.functions.initialization;
 
 public enum Initialization {
-    //Xavier and HE
     XAVIER {
         @Override
         public double[] weightInitialization(int inputSize, double[] weights) {

lib/src/main/java/de/edux/ml/nn/network/MultilayerPerceptron.java

@@ -88,22 +88,32 @@ public MultilayerPerceptron(NetworkConfiguration config, double[][] testFeatures
     }
 
     private double[] feedforward(double[] input) {
-        double[] currentInput = input;
 
+        double[] currentInput = passInputTroughAllHiddenLayers(input);
+
+        double[] output = passInputTroughOutputLayer(currentInput);
+
+        return outputLayerActivationFunction.calculateActivation(output);
+    }
+
+    private double[] passInputTroughAllHiddenLayers(double[] input) {
+        double[] currentInput = input;
         for (Neuron[] layer : hiddenLayers) {
             double[] hiddenOutputs = new double[layer.length];
             for (int i = 0; i < layer.length; i++) {
                 hiddenOutputs[i] = layer[i].calculateOutput(currentInput);
             }
             currentInput = hiddenOutputs;
         }
+        return currentInput;
+    }
 
+    private double[] passInputTroughOutputLayer(double[] currentInput) {
         double[] output = new double[config.outputSize()];
         for (int i = 0; i < config.outputSize(); i++) {
             output[i] = outputLayer[i].calculateOutput(currentInput);
         }
-
-        return outputLayerActivationFunction.calculateActivation(output);
+        return output;
     }
 
     @Override

lib/src/main/java/de/edux/ml/randomforest/RandomForest.java

@@ -129,7 +129,6 @@ public double[] predict(double[] feature) {
         for (Future<double[]> future : futures) {
             try {
                 double[] prediction = future.get();
-                /*               voteMap.merge(prediction, 1L, Long::sum);*/
                 double label = getIndexOfHighestValue(prediction);
                 voteMap.merge(label, 1L, Long::sum);
             } catch (InterruptedException | ExecutionException e) {

lib/src/main/java/de/edux/ml/svm/ISupportVectorMachine.java

@@ -4,9 +4,7 @@ public interface ISupportVectorMachine {
 
     void train(double[][] features, int[] labels);
 
-    // Methode zum Klassifizieren eines einzelnen Datenpunkts
     int predict(double[] features);
 
-    // Methode zum Evaluieren der Leistung der SVM auf einem Testdatensatz
     double evaluate(double[][] features, int[] labels);
 }

lib/src/main/java/de/edux/ml/svm/SupportVectorMachine.java

@@ -42,30 +42,25 @@ public SupportVectorMachine(SVMKernel kernel, double c) {
     @Override
     public boolean train(double[][] features, double[][] labels) {
         var oneDLabels = convert2DLabelArrayTo1DLabelArray(labels);
-        // Identify unique class labels
         Set<Integer> uniqueLabels = Arrays.stream(oneDLabels).boxed().collect(Collectors.toSet());
         Integer[] uniqueLabelsArray = uniqueLabels.toArray(new Integer[0]);
 
-        // In One-vs-One, you should consider every possible pair of classes
         for (int i = 0; i < uniqueLabelsArray.length; i++) {
             for (int j = i + 1; j < uniqueLabelsArray.length; j++) {
                 String key = uniqueLabelsArray[i] + "-" + uniqueLabelsArray[j];
                 SVMModel model = new SVMModel(kernel, c);
 
-                // Filter the features and labels for the two classes
                 List<double[]> list = new ArrayList<>();
                 List<Integer> pairLabelsList = new ArrayList<>();
                 for (int k = 0; k < features.length; k++) {
                     if (oneDLabels[k] == uniqueLabelsArray[i] || oneDLabels[k] == uniqueLabelsArray[j]) {
                         list.add(features[k]);
-                        // Ensure that the sign of the label matches our assumption
                         pairLabelsList.add(oneDLabels[k] == uniqueLabelsArray[i] ? 1 : -1);
                     }
                 }
                 double[][] pairFeatures = list.toArray(new double[0][]);
                 int[] pairLabels = pairLabelsList.stream().mapToInt(Integer::intValue).toArray();
 
-                // Train the model on the pair
                 model.train(pairFeatures, pairLabels);
                 models.put(key, model);
             }
@@ -76,18 +71,16 @@ public boolean train(double[][] features, double[][] labels) {
     @Override
     public double[] predict(double[] features) {
         Map<Integer, Integer> voteCount = new HashMap<>();
-        // In One-vs-One, you look at the prediction of each model and count the votes
+
         for (Map.Entry<String, SVMModel> entry : models.entrySet()) {
             int prediction = entry.getValue().predict(features);
 
-            // map prediction back to actual class label
             String[] classes = entry.getKey().split("-");
             int classLabel = (prediction == 1) ? Integer.parseInt(classes[0]) : Integer.parseInt(classes[1]);
 
             voteCount.put(classLabel, voteCount.getOrDefault(classLabel, 0) + 1);
         }
 
-        // The final prediction is the class with the most votes
         int prediction = voteCount.entrySet().stream().max(Map.Entry.comparingByValue()).get().getKey();
         double[] result = new double[models.size()];
         result[prediction - 1] = 1;

lib/src/test/java/de/edux/data/provider/DataProcessorTest.java

@@ -105,7 +105,6 @@ private DataProcessor<String> getDummyDataUtil() {
 
             @Override
             public void normalize(List<String> dataset) {
-                // Mock normalize for the sake of testing
             }
 
             @Override

lib/src/test/java/de/edux/data/provider/SeabornDataProcessor.java

@@ -143,7 +143,7 @@ public double[][] getTestLabels() {
     }
 
     private double[][] featuresOf(List<Penguin> data) {
-        double[][] features = new double[data.size()][4]; // 4 numerische Eigenschaften
+        double[][] features = new double[data.size()][4];
 
         for (int i = 0; i < data.size(); i++) {
             Penguin p = data.get(i);
@@ -157,7 +157,7 @@ private double[][] featuresOf(List<Penguin> data) {
     }
 
     private double[][] labelsOf(List<Penguin> data) {
-        double[][] labels = new double[data.size()][3]; // 3 Pinguinarten
+        double[][] labels = new double[data.size()][3];
 
         for (int i = 0; i < data.size(); i++) {
             Penguin p = data.get(i);

lib/src/test/java/de/edux/data/provider/SeabornProvider.java

@@ -50,7 +50,7 @@ public double[][] getTrainFeatures() {
     }
 
     private double[][] featuresOf(List<Penguin> data) {
-        double[][] features = new double[data.size()][4]; // 4 numerische Eigenschaften
+        double[][] features = new double[data.size()][4];
 
         for (int i = 0; i < data.size(); i++) {
             Penguin p = data.get(i);
@@ -70,7 +70,7 @@ public double[][] getTrainLabels() {
     }
 
     private double[][] labelsOf(List<Penguin> data) {
-        double[][] labels = new double[data.size()][3]; // 3 Pinguinarten
+        double[][] labels = new double[data.size()][3];
 
         for (int i = 0; i < data.size(); i++) {
             Penguin p = data.get(i);

lib/src/test/java/de/edux/edux/activation/ActivationFunctionTest.java

@@ -8,7 +8,7 @@
 
 public class ActivationFunctionTest {
 
-    private static final double DELTA = 1e-6; // used to compare floating point numbers
+    private static final double DELTA = 1e-6;
 
     @Test
     public void testSigmoid() {