Change signature of train method

src/main/java/com/naaturel/ANN/Main.java

@@ -6,8 +6,10 @@ import com.naaturel.ANN.implementation.multiLayers.Sigmoid;
 import com.naaturel.ANN.implementation.multiLayers.TanH;
 import com.naaturel.ANN.implementation.training.GradientBackpropagationTraining;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
+import com.naaturel.ANN.infrastructure.dataset.DataSetEntry;
 import com.naaturel.ANN.infrastructure.dataset.DatasetExtractor;
 import com.naaturel.ANN.domain.model.neuron.*;
+import com.naaturel.ANN.infrastructure.graph.GraphVisualizer;
 
 import java.util.*;
 
@@ -15,13 +17,13 @@ public class Main {
 
     public static void main(String[] args){
 
-        int nbrInput = 25;
+        int nbrClass = 1;
-        int nbrClass = 4;
-
-        int[] neuronPerLayer = new int[]{10, nbrClass};
 
         DataSet dataset = new DatasetExtractor()
-                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_3_5.csv", nbrClass);
+                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_4_12.csv", nbrClass);
+
+        int[] neuronPerLayer = new int[]{10, dataset.getNbrLabels()};
+        int nbrInput = dataset.getNbrInputs();
 
         List<Layer> layers = new ArrayList<>();
         for (int i = 0; i < neuronPerLayer.length; i++){
@@ -38,7 +40,7 @@ public class Main {
 
                 Bias bias =  new Bias(new Weight());
 
-                Neuron n = new Neuron(syns, bias, new TanH());
+                Neuron n = new Neuron(syns, bias, new Sigmoid(2));
                 neurons.add(n);
             }
             Layer layer = new Layer(neurons);
@@ -48,7 +50,28 @@ public class Main {
         FullyConnectedNetwork network = new FullyConnectedNetwork(layers);
 
         Trainer trainer = new GradientBackpropagationTraining();
-        trainer.train(network, dataset);
+        trainer.train(0.5F, network, dataset);
 
+        /*GraphVisualizer visualizer = new GraphVisualizer();
+
+        for (DataSetEntry entry : dataset) {
+            List<Float> label = dataset.getLabelsAsFloat(entry);
+            visualizer.addPoint("Label " + label.getFirst(), entry.getData().get(0).getValue(), entry.getData().get(1).getValue());
+        }
+
+        float min = -2F;
+        float max = 2F;
+        float step = 0.01F;
+        for (float x = min; x < max; x+=step){
+            for (float y = min; y < max; y+=step){
+                float prediction = network.predict(List.of(new Input(x), new Input(y))).getFirst();
+                float predSeries = prediction > 0.5F ? 1 : 0;
+                visualizer.addPoint(Float.toString(predSeries), x, y);
+            }
+        }
+
+
+        visualizer.buildScatterGraph();*/
     }
+
 }

src/main/java/com/naaturel/ANN/domain/abstraction/Trainer.java

@@ -3,5 +3,5 @@ package com.naaturel.ANN.domain.abstraction;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
 
 public interface Trainer {
-    void train(Model model, DataSet dataset);
+    void train(float learningRate, Model model, DataSet dataset);
 }

src/main/java/com/naaturel/ANN/domain/model/neuron/Neuron.java

@@ -53,12 +53,11 @@ public class Neuron implements Model {
     }
 
     public float calculateWeightedSum() {
-        float res = 0;
+        this.weightedSum = 0F;
-        res += this.bias.getWeight() * this.bias.getInput();
+        this.weightedSum += this.bias.getWeight() * this.bias.getInput();
         for(Synapse syn : this.synapses){
-            res += syn.getWeight() * syn.getInput();
+            this.weightedSum += syn.getWeight() * syn.getInput();
         }
-        this.weightedSum = res;
         return this.weightedSum;
     }
 

src/main/java/com/naaturel/ANN/domain/model/training/TrainingPipeline.java

@@ -124,7 +124,7 @@ public class TrainingPipeline {
             this.visualizer.addPoint("p"+i, inputs.get(0).getValue()+0.01F, inputs.get(1).getValue()+0.01F);
             i++;
         }
-        this.visualizer.build();
+        this.visualizer.buildLineGraph();
     }
 
 }

src/main/java/com/naaturel/ANN/implementation/training/AdalineTraining.java

@@ -23,11 +23,11 @@ public class AdalineTraining implements Trainer {
     }
 
     @Override
-    public void train(Model model, DataSet dataset) {
+    public void train(float learningRate, Model model, DataSet dataset) {
         AdalineTrainingContext context = new AdalineTrainingContext();
         context.dataset = dataset;
         context.model = model;
-        context.learningRate = 0.003F;
+        context.learningRate = learningRate;
 
         List<AlgorithmStep> steps = List.of(
                 new SimplePredictionStep(context),

src/main/java/com/naaturel/ANN/implementation/training/GradientBackpropagationTraining.java

@@ -13,14 +13,13 @@ import com.naaturel.ANN.infrastructure.dataset.DataSet;
 
 import java.util.List;
 
-
 public class GradientBackpropagationTraining implements Trainer {
     @Override
-    public void train(Model model, DataSet dataset) {
+    public void train(float learningRate, Model model, DataSet dataset) {
         GradientBackpropagationContext context = new GradientBackpropagationContext();
         context.dataset = dataset;
         context.model = model;
-        context.learningRate = 0.1F;
+        context.learningRate = learningRate;
 
         List<AlgorithmStep> steps = List.of(
                 new SimplePredictionStep(context),
@@ -30,8 +29,10 @@ public class GradientBackpropagationTraining implements Trainer {
         );
 
         new TrainingPipeline(steps)
-                .stopCondition(ctx -> ctx.epoch == 250)
+                .beforeEpoch(ctx -> ctx.globalLoss = 0.0F)
-                .withVerbose(true)
+                .afterEpoch(ctx -> ctx.globalLoss = ctx.localLoss/dataset.size())
+                .stopCondition(ctx -> ctx.epoch > 1000000)
+                .withVerbose(false)
                 .withTimeMeasurement(true)
                 .run(context);
 

src/main/java/com/naaturel/ANN/implementation/training/GradientDescentTraining.java

@@ -23,11 +23,11 @@ public class GradientDescentTraining implements Trainer {
     }
 
     @Override
-    public void train(Model model, DataSet dataset) {
+    public void train(float learningRate, Model model, DataSet dataset) {
         GradientDescentTrainingContext context = new GradientDescentTrainingContext();
         context.dataset = dataset;
         context.model = model;
-        context.learningRate = 0.0008F;
+        context.learningRate = learningRate;
         context.correctorTerms =  new ArrayList<>();
 
         List<AlgorithmStep> steps = List.of(

src/main/java/com/naaturel/ANN/implementation/training/SimpleTraining.java

@@ -16,11 +16,11 @@ public class SimpleTraining implements Trainer {
     }
 
     @Override
-    public void train(Model model, DataSet dataset) {
+    public void train(float learningRate, Model model, DataSet dataset) {
         SimpleTrainingContext context = new SimpleTrainingContext();
         context.dataset = dataset;
         context.model = model;
-        context.learningRate = 0.3F;
+        context.learningRate = learningRate;
 
         List<AlgorithmStep> steps = List.of(
                 new SimplePredictionStep(context),

src/main/java/com/naaturel/ANN/infrastructure/dataset/DataSet.java

@@ -3,23 +3,52 @@ package com.naaturel.ANN.infrastructure.dataset;
 import com.naaturel.ANN.domain.model.neuron.Input;
 
 import java.util.*;
+import java.util.stream.Stream;
 
 public class DataSet implements Iterable<DataSetEntry>{
 
     private final Map<DataSetEntry, Labels> data;
 
+    private final int nbrInputs;
+    private final int nbrLabels;
+
     public DataSet() {
-        this(new LinkedHashMap<>());
+        this(new LinkedHashMap<>()); //ensure iteration order is the same as insertion order
     }
 
     public DataSet(Map<DataSetEntry, Labels> data){
         this.data = data;
+        this.nbrInputs = this.calculateNbrInput();
+        this.nbrLabels = this.calculateNbrLabel();
     }
 
+    private int calculateNbrInput(){
+        //assumes every entry are the same length
+        Stream<DataSetEntry> keyStream = this.data.keySet().stream();
+        Optional<DataSetEntry> firstEntry = keyStream.findFirst();
+        return firstEntry.map(inputs -> inputs.getData().size()).orElse(0);
+    }
+
+    private int calculateNbrLabel(){
+        //assumes every label are the same length
+        Stream<DataSetEntry> keyStream = this.data.keySet().stream();
+        Optional<DataSetEntry> firstEntry = keyStream.findFirst();
+        return firstEntry.map(inputs -> this.data.get(inputs).getValues().size()).orElse(0);
+    }
+
+
     public int size() {
         return data.size();
     }
 
+    public int getNbrInputs() {
+        return this.nbrInputs;
+    }
+
+    public int getNbrLabels(){
+        return this.nbrLabels;
+    }
+
     public List<DataSetEntry> getData(){
         return new ArrayList<>(this.data.keySet());
     }

src/main/java/com/naaturel/ANN/infrastructure/graph/GraphVisualizer.java

@@ -3,6 +3,7 @@ package com.naaturel.ANN.infrastructure.graph;
 import org.jfree.chart.ChartFactory;
 import org.jfree.chart.ChartPanel;
 import org.jfree.chart.JFreeChart;
+import org.jfree.chart.plot.XYPlot;
 import org.jfree.data.xy.XYSeries;
 import org.jfree.data.xy.XYSeriesCollection;
 
@@ -29,7 +30,7 @@ public class GraphVisualizer {
         }
     }
 
-    public void build(){
+    public void buildLineGraph(){
         JFreeChart chart = ChartFactory.createXYLineChart(
                 "Model learning", "X", "Y", dataset
         );
@@ -39,4 +40,21 @@ public class GraphVisualizer {
         frame.pack();
         frame.setVisible(true);
     }
+
+
+    public void buildScatterGraph(){
+        JFreeChart chart = ChartFactory.createScatterPlot(
+                "Predictions", "X", "Y", dataset
+        );
+        XYPlot plot = chart.getXYPlot();
+        plot.getDomainAxis().setRange(-2, 2);
+        plot.getRangeAxis().setRange(-2, 2);
+
+        JFrame frame = new JFrame("Predictions");
+        frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE);
+        frame.add(new ChartPanel(chart));
+        frame.pack();
+        frame.setVisible(true);
+    }
+
 }

src/main/resources/assets/xor.csv

@@ -0,0 +1,4 @@
+0,0,0
+0,1,1
+1,0,1
+1,1,0