Just a regular commit

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

@@ -1,6 +1,7 @@
 package com.naaturel.ANN;
 
 import com.naaturel.ANN.domain.abstraction.Neuron;
+import com.naaturel.ANN.domain.abstraction.Trainer;
 import com.naaturel.ANN.domain.model.dataset.DataSet;
 import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
 import com.naaturel.ANN.domain.model.dataset.Label;
@@ -8,7 +9,6 @@ import com.naaturel.ANN.domain.model.neuron.Bias;
 import com.naaturel.ANN.domain.model.neuron.Input;
 import com.naaturel.ANN.domain.model.neuron.Synapse;
 import com.naaturel.ANN.domain.model.neuron.Weight;
-import com.naaturel.ANN.implementation.activationFunction.Heaviside;
 import com.naaturel.ANN.implementation.activationFunction.Linear;
 import com.naaturel.ANN.implementation.neuron.SimplePerceptron;
 import com.naaturel.ANN.implementation.training.AdalineTraining;
@@ -29,8 +29,8 @@ public class Main {
 
         DataSet andDataSet = new DataSet(Map.ofEntries(
                 Map.entry(new DataSetEntry(List.of(0.0F, 0.0F)), new Label(-1.0F)),
-                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(0.0F, 1.0F)), new Label(-1.0F)),
                 Map.entry(new DataSetEntry(List.of(1.0F, 1.0F)), new Label(1.0F))
         ));
 
@@ -65,11 +65,11 @@ public class Main {
         Bias bias =  new Bias(new Weight(0));
 
         Neuron n = new SimplePerceptron(syns, bias, new Linear());
-        AdalineTraining st = new AdalineTraining();
+        Trainer trainer = new AdalineTraining();
 
         long start = System.currentTimeMillis();
 
-        st.train(n, 0.03F, andDataSet);
+        trainer.train(n, 0.03F, andDataSet);
 
         long end = System.currentTimeMillis();
         System.out.printf("Training completed in %.2f s%n", (end - start) / 1000.0);

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

@@ -0,0 +1,8 @@
+package com.naaturel.ANN.domain.abstraction;
+
+import com.naaturel.ANN.domain.model.dataset.DataSet;
+
+public interface Trainer {
+
+    void train(Neuron n, float learningRate, DataSet dataSet);
+}

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

@@ -27,4 +27,6 @@ public class Synapse {
     }
 
 
+
+
 }

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

@@ -1,17 +1,15 @@
 package com.naaturel.ANN.implementation.training;
 
 import com.naaturel.ANN.domain.abstraction.Neuron;
+import com.naaturel.ANN.domain.abstraction.Trainer;
 import com.naaturel.ANN.domain.model.dataset.DataSet;
 import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
-import com.naaturel.ANN.domain.model.neuron.Bias;
 import com.naaturel.ANN.domain.model.neuron.Input;
 import com.naaturel.ANN.domain.model.neuron.Synapse;
 import com.naaturel.ANN.domain.model.neuron.Weight;
 
-import java.util.ArrayList;
-import java.util.List;
 
-public class AdalineTraining {
+public class AdalineTraining implements Trainer {
 
     public AdalineTraining(){
 
@@ -19,15 +17,14 @@ public class AdalineTraining {
 
     public void train(Neuron n, float learningRate, DataSet dataSet) {
         int epoch = 1;
-        int maxEpoch = 1000;
+        int maxEpoch = 202;
         float errorThreshold = 0.0F;
         float mse;
 
         do {
             if(epoch > maxEpoch) break;
             mse = 0;
-
+             for(DataSetEntry entry : dataSet) {
-            for(DataSetEntry entry : dataSet) {
                 this.updateInputs(n, entry);
                 float prediction = n.predict();
                 float expectation = dataSet.getLabel(entry).getValue();
@@ -49,23 +46,22 @@ public class AdalineTraining {
                 System.out.printf("predicted : %.2f, ", prediction);
                 System.out.printf("expected : %.2f, ", expectation);
                 System.out.printf("delta : %.2f, ", delta);
-                System.out.printf("loss : %.2f\n", loss);
+                System.out.printf("loss : %.5f\n", loss);
             }
+            mse /= dataSet.size();
             System.out.printf("[Total error : %f]\n", mse);
-
+            System.out.println("[Final weights]");
+            System.out.printf("Bias: %f\n", n.getBias().getWeight());
+            int i = 1;
+            for(Synapse syn : n.getSynapses()){
+                System.out.printf("Syn %d: %f\n", i, syn.getWeight());
+                i++;
+            }
             epoch++;
         } while(mse > errorThreshold);
 
     }
 
-    private List<Float> initCorrectorTerms(int number){
-        List<Float> res = new ArrayList<>();
-        for(int i = 0; i < number; i++){
-            res.add(0F);
-        }
-        return res;
-    }
-
     private void updateInputs(Neuron n, DataSetEntry entry){
         int index = 0;
         for(float value : entry){
@@ -82,8 +78,4 @@ public class AdalineTraining {
         return (float) Math.pow(delta, 2)/2;
     }
 
-    private float calculateWeightCorrection(float value, float delta){
-        return value * delta;
-    }
-
 }

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

@@ -1,6 +1,7 @@
 package com.naaturel.ANN.implementation.training;
 
 import com.naaturel.ANN.domain.abstraction.Neuron;
+import com.naaturel.ANN.domain.abstraction.Trainer;
 import com.naaturel.ANN.domain.model.dataset.DataSet;
 import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
 import com.naaturel.ANN.domain.model.neuron.Bias;
@@ -9,9 +10,10 @@ import com.naaturel.ANN.domain.model.neuron.Synapse;
 import com.naaturel.ANN.domain.model.neuron.Weight;
 
 import java.util.ArrayList;
+import java.util.Arrays;
 import java.util.List;
 
-public class GradientDescentTraining {
+public class GradientDescentTraining implements Trainer {
 
     public GradientDescentTraining(){
 
@@ -19,8 +21,8 @@ public class GradientDescentTraining {
 
     public void train(Neuron n, float learningRate, DataSet dataSet) {
         int epoch = 1;
-        int maxEpoch = 1000;
+        int maxEpoch = 402;
-        float errorThreshold = 0.0F;
+        float errorThreshold = 0F;
         float mse;
 
         do {
@@ -54,6 +56,7 @@ public class GradientDescentTraining {
                 System.out.printf("delta : %.2f, ", delta);
                 System.out.printf("loss : %.2f\n", loss);
             }
+            mse /= dataSet.size();
             System.out.printf("[Total error : %f]\n", mse);
 
             float currentBias = n.getBias().getWeight();
@@ -69,6 +72,13 @@ public class GradientDescentTraining {
             epoch++;
         } while(mse > errorThreshold);
 
+        System.out.println("[Final weights]");
+        System.out.printf("Bias: %f\n", n.getBias().getWeight());
+        int i = 1;
+        for(Synapse syn : n.getSynapses()){
+            System.out.printf("Syn %d: %f\n", i, syn.getWeight());
+            i++;
+        }
     }
 
     private List<Float> initCorrectorTerms(int number){
@@ -95,8 +105,21 @@ public class GradientDescentTraining {
         return (float) Math.pow(delta, 2)/2;
     }
 
-    private float calculateWeightCorrection(float value, float delta){
+    public float computeThreshold(DataSet dataSet) {
-        return value * delta;
+        float sum = 0;
+        for (DataSetEntry entry : dataSet) {
+            sum += dataSet.getLabel(entry).getValue();
+        }
+        float mean = sum / dataSet.size();
+
+        float variance = 0;
+        for (DataSetEntry entry : dataSet) {
+            float diff = dataSet.getLabel(entry).getValue() - mean;
+            variance += diff * diff;
+        }
+        variance /= dataSet.size();
+
+        return variance;
     }
 
 }

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

@@ -1,13 +1,14 @@
 package com.naaturel.ANN.implementation.training;
 
 import com.naaturel.ANN.domain.abstraction.Neuron;
+import com.naaturel.ANN.domain.abstraction.Trainer;
 import com.naaturel.ANN.domain.model.dataset.DataSet;
 import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
 import com.naaturel.ANN.domain.model.neuron.Input;
 import com.naaturel.ANN.domain.model.neuron.Synapse;
 import com.naaturel.ANN.domain.model.neuron.Weight;
 
-public class SimpleTraining {
+public class SimpleTraining implements Trainer {
 
     public SimpleTraining() {