Multilayerperceptron example added with seaborndataset

example/gradle/wrapper/gradle-wrapper.properties

@@ -0,0 +1,5 @@
+distributionBase=GRADLE_USER_HOME
+distributionPath=wrapper/dists
+distributionUrl=https\://services.gradle.org/distributions/gradle-4.5-bin.zip
+zipStoreBase=GRADLE_USER_HOME
+zipStorePath=wrapper/dists

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

@@ -0,0 +1,50 @@
+package de.example.nn;
+
+
+import de.edux.api.Classifier;
+import de.edux.functions.activation.ActivationFunction;
+import de.edux.functions.initialization.Initialization;
+import de.edux.functions.loss.LossFunction;
+import de.edux.ml.knn.KnnClassifier;
+import de.edux.ml.nn.config.NetworkConfiguration;
+import de.edux.ml.nn.network.MultilayerPerceptron;
+import de.edux.ml.nn.network.api.Dataset;
+import de.example.data.seaborn.Penguin;
+import de.example.data.seaborn.SeabornDataProcessor;
+import de.example.data.seaborn.SeabornProvider;
+
+import java.io.File;
+import java.util.List;
+
+/**
+ * Knn - K nearest neighbors
+ * Dataset: Seaborn Penguins
+ */
+public class MultilayerPerceptronExample2 {
+    private static final boolean SHUFFLE = true;
+    private static final boolean NORMALIZE = true;
+    private static final boolean FILTER_INCOMPLETE_RECORDS = true;
+    private static final double TRAIN_TEST_SPLIT_RATIO = 0.75;
+    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) {
+        var seabornDataProcessor = new SeabornDataProcessor();
+        List<Penguin> data = seabornDataProcessor.loadDataSetFromCSV(CSV_FILE, ',', SHUFFLE, NORMALIZE, FILTER_INCOMPLETE_RECORDS);
+
+        Dataset<Penguin> dataset = seabornDataProcessor.split(data, TRAIN_TEST_SPLIT_RATIO);
+        var seabornProvider = new SeabornProvider(data, dataset.trainData(), dataset.testData());
+        seabornProvider.printStatistics();
+        double[][] features = seabornProvider.getTrainFeatures();
+        double[][] labels = seabornProvider.getTrainLabels();
+
+        double[][] testFeatures = seabornProvider.getTestFeatures();
+        double[][] testLabels = seabornProvider.getTestLabels();
+
+        NetworkConfiguration networkConfiguration = new NetworkConfiguration(seabornProvider.getTrainFeatures()[0].length, List.of(32, 6), 3, 0.01, 1000, ActivationFunction.LEAKY_RELU, ActivationFunction.SOFTMAX, LossFunction.CATEGORICAL_CROSS_ENTROPY, Initialization.XAVIER, Initialization.XAVIER);
+
+        MultilayerPerceptron multilayerPerceptron = new MultilayerPerceptron(networkConfiguration, testFeatures, testLabels);
+        multilayerPerceptron.train(seabornProvider.getTrainFeatures(), labels);
+        multilayerPerceptron.evaluate(testFeatures, testLabels);
+    }
+
+}