Added dataset anf fixed gradient descent algorithm

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

@@ -19,6 +19,13 @@ public class Main {
 
     public static void main(String[] args){
 
+        DataSet orDataSet = new DataSet(Map.ofEntries(
+                Map.entry(new DataSetEntry(List.of(0.0F, 0.0F)), new Label(0.0F)),
+                Map.entry(new DataSetEntry(List.of(0.0F, 1.0F)), new Label(1.0F)),
+                Map.entry(new DataSetEntry(List.of(1.0F, 0.0F)), new Label(1.0F)),
+                Map.entry(new DataSetEntry(List.of(1.0F, 1.0F)), new Label(1.0F))
+        ));
+
         DataSet dataSet = new DataSet(Map.ofEntries(
                 Map.entry(new DataSetEntry(List.of(1.0F, 6.0F)), new Label(1.0F)),
                 Map.entry(new DataSetEntry(List.of(7.0F, 9.0F)), new Label(-1.0F)),
@@ -51,7 +58,12 @@ public class Main {
 
         Neuron n = new SimplePerceptron(syns, bias, new Linear());
         GradientDescentTraining st = new GradientDescentTraining();
-        st.train(n, 0.0003F, dataSet);
-    }
 
+        long start = System.currentTimeMillis();
+
+        st.train(n, 0.03F, orDataSet.toNormalized());
+
+        long end = System.currentTimeMillis();
+        System.out.printf("Training completed in %.2f s%n", (end - start) / 1000.0);
+    }
 }

src/main/java/com/naaturel/ANN/domain/model/dataset/DataSet.java

@@ -22,6 +22,26 @@ public class DataSet implements Iterable<DataSetEntry>{
         return this.data.get(entry);
     }
 
+    public DataSet toNormalized() {
+        List<DataSetEntry> entries = this.getData();
+
+        float maxAbs = entries.stream()
+                .flatMap(e -> e.getData().stream())
+                .map(Math::abs)
+                .max(Float::compare)
+                .orElse(1.0F);
+
+        Map<DataSetEntry, Label> normalized = new HashMap<>();
+        for (DataSetEntry entry : entries) {
+            List<Float> normalizedData = new ArrayList<>();
+            for (float value : entry.getData()) {
+                normalizedData.add(Math.round((value / maxAbs) * 100.0F) / 100.0F);
+            }
+            normalized.put(new DataSetEntry(normalizedData), this.data.get(entry));
+        }
+
+        return new DataSet(normalized);
+    }
 
     @Override
     public Iterator<DataSetEntry> iterator() {

src/main/java/com/naaturel/ANN/implementation/neuron/SimplePerceptron.java

@@ -25,7 +25,7 @@ public class SimplePerceptron extends Neuron implements Trainable {
         for(Synapse syn : super.synapses){
             res += syn.getWeight() * syn.getInput();
         }
-
+        res += this.bias.getWeight() * this.bias.getInput();
         return res;
     }
 

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

@@ -38,6 +38,7 @@ public class GradientDescentTraining {
                 float loss = this.calculateLoss(delta);
 
                 currentError += loss;
+
                 Bias b = n.getBias();
                 biasCorrector += this.calculateWeightCorrection(learningRate, b.getInput(), delta);
 
@@ -47,6 +48,8 @@ public class GradientDescentTraining {
                     c += this.calculateWeightCorrection(learningRate, syn.getInput(), delta);
                     correctorTerms.set(i, c);
                 }
+
+                if(epoch % 10 != 0) continue;
                 System.out.printf("Epoch : %d ", epoch);
                 System.out.printf("predicted : %.2f, ", prediction);
                 System.out.printf("expected : %.2f, ", expectation);
@@ -54,13 +57,15 @@ public class GradientDescentTraining {
                 System.out.printf("loss : %.2f\n", loss);
 
             }
-            System.out.printf("[Total error : %.2f]\n", currentError);
+
+            System.out.printf("[Total error : %.3f]\n", currentError);
+            biasCorrector += n.getBias().getWeight();
             n.updateBias(new Weight(biasCorrector));
 
             for(int i = 0; i < correctorTerms.size(); i++){
                 Synapse syn = n.getSynapse(i);
-                float c = correctorTerms.get(i);
-                syn.setWeight(syn.getWeight() + c);
+                float c = syn.getWeight() + correctorTerms.get(i);
+                syn.setWeight(c);
             }
 
             epoch++;