Fix restoration of activation function

config.json

@@ -5,7 +5,7 @@
     "path": "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/snapshots/snapshot-test.json"
   },
   "training" : {
-    "learning_rate" : 0.03,
+    "learning_rate" : 1.0,
     "max_epoch" : 5000
   },
   "dataset" : {

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

@@ -101,6 +101,7 @@ public class Main {
         //plot predictions
         for (float x = min; x < max; x+=step){
             for (float y = min; y < max; y+=step){
+
                 List<Float> predictions = new ArrayList<>();
                 for (float p : network.predict(new float[]{x, y})) predictions.add(p);
 

src/main/java/com/naaturel/ANN/domain/model/helpers/ModelCreator.java

@@ -3,12 +3,23 @@ package com.naaturel.ANN.domain.model.helpers;
 import com.naaturel.ANN.domain.abstraction.ActivationFunction;
 import com.naaturel.ANN.domain.abstraction.Model;
 import com.naaturel.ANN.domain.model.neuron.*;
+import com.naaturel.ANN.implementation.simplePerceptron.Heaviside;
 
 import java.util.ArrayList;
 import java.util.List;
 
 public class ModelCreator {
 
+    public static Model createPerceptron(int nbrInputs){
+        List<Synapse> syns = new ArrayList<>();
+        for (int k = 0; k < nbrInputs; k++){
+            syns.add(new Synapse(new Input(0), new Weight(0)));
+        }
+
+        Bias bias =  new Bias(new Weight(0));
+        return new Neuron(0, syns.toArray(new Synapse[0]), bias, new Heaviside());
+    }
+
     public static Model createModel(int[] neuronPerLayer, int nbrInput, ActivationFunction func){
         int neuronId = 0;
         List<Layer> layers = new ArrayList<>();

src/main/java/com/naaturel/ANN/implementation/gradientDescent/Linear.java

@@ -23,4 +23,8 @@ public class Linear implements ActivationFunction {
         return this.slope;
     }
 
+    @Override
+    public String toString() {
+        return "linear";
+    }
 }

src/main/java/com/naaturel/ANN/implementation/multiLayers/Sigmoid.java

@@ -5,7 +5,7 @@ import com.naaturel.ANN.domain.model.neuron.Neuron;
 
 public class Sigmoid implements ActivationFunction {
 
-    private float steepness;
+    private final float steepness;
 
     public Sigmoid(float steepness) {
         this.steepness = steepness;
@@ -20,4 +20,9 @@ public class Sigmoid implements ActivationFunction {
     public float derivative(float value) {
         return steepness * value * (1 - value);
     }
+
+    @Override
+    public String toString() {
+        return "sigmoid";
+    }
 }

src/main/java/com/naaturel/ANN/implementation/multiLayers/TanH.java

@@ -18,4 +18,9 @@ public class TanH implements ActivationFunction {
     public float derivative(float value) {
         return 1 - value * value;
     }
+
+    @Override
+    public String toString() {
+        return "tanh";
+    }
 }

src/main/java/com/naaturel/ANN/implementation/simplePerceptron/Heaviside.java

@@ -21,4 +21,10 @@ public class Heaviside implements ActivationFunction {
     public float derivative(float value) {
         throw new UnsupportedOperationException("Heaviside is not differentiable");
     }
+
+
+    @Override
+    public String toString() {
+        return "heaviside";
+    }
 }

src/main/java/com/naaturel/ANN/implementation/simplePerceptron/SimpleErrorRegistrationStep.java

@@ -13,6 +13,6 @@ public class SimpleErrorRegistrationStep implements AlgorithmStep {
 
     @Override
     public void run() {
-        context.globalLoss += context.localLoss;
+        context.globalLoss += context.localLoss == 0 ? 0 : 1;
     }
 }

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

@@ -45,7 +45,7 @@ public class AdalineTraining implements Trainer {
                 .beforeEpoch(ctx -> ctx.globalLoss = 0.0F)
                 .afterEpoch(ctx -> ctx.globalLoss /= context.dataset.size())
                 .withTimeMeasurement(true)
-                .withVerbose(true, 1)
+                .withVerbose(true, 500)
                 .withVisualization(true, new GraphVisualizer())
                 .run(context);
     }

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

@@ -16,7 +16,7 @@ public class SimpleTraining implements Trainer {
     private Model model;
 
     public SimpleTraining(int[] neurons, int nbrInputs){
-        model = ModelCreator.createModel(neurons, nbrInputs, new Heaviside());
+        model = ModelCreator.createPerceptron(nbrInputs);
     }
 
     @Override

src/main/java/com/naaturel/ANN/infrastructure/persistence/ModelSnapshot.java

@@ -3,9 +3,13 @@ package com.naaturel.ANN.infrastructure.persistence;
 import com.fasterxml.jackson.databind.ObjectMapper;
 import com.fasterxml.jackson.databind.node.ArrayNode;
 import com.fasterxml.jackson.databind.node.ObjectNode;
+import com.naaturel.ANN.domain.abstraction.ActivationFunction;
 import com.naaturel.ANN.domain.abstraction.Model;
 import com.naaturel.ANN.domain.model.neuron.*;
+import com.naaturel.ANN.implementation.gradientDescent.Linear;
+import com.naaturel.ANN.implementation.multiLayers.Sigmoid;
 import com.naaturel.ANN.implementation.multiLayers.TanH;
+import com.naaturel.ANN.implementation.simplePerceptron.Heaviside;
 
 import java.io.File;
 import java.util.ArrayList;
@@ -28,6 +32,7 @@ public class ModelSnapshot {
 
             ObjectNode neuronNode = mapper.createObjectNode();
             neuronNode.put("id", n.getId());
+            neuronNode.put("func", n.getActivationFunction().toString());
             neuronNode.put("layerIndex", model.layerIndexOf(n));
 
             ArrayNode weights = mapper.createArrayNode();
@@ -50,6 +55,13 @@ public class ModelSnapshot {
         root.forEach(neuronNode -> {
             int id = neuronNode.get("id").asInt();
             int layerIndex = neuronNode.get("layerIndex").asInt();
+            ActivationFunction func = switch (neuronNode.get("func").asText()){
+                case "heaviside" -> new Heaviside();
+                case "linear" -> new Linear(1, 0);
+                case "sigmoid" -> new Sigmoid(1);
+                case "tanh" -> new TanH();
+                default -> throw new IllegalStateException("Unexpected value: " + neuronNode.get("func").asText());
+            };
             ArrayNode weightsNode = (ArrayNode) neuronNode.get("weights");
 
             Bias bias = new Bias(new Weight(weightsNode.get(0).floatValue()));
@@ -58,7 +70,7 @@ public class ModelSnapshot {
                 synapses[i] = new Synapse(new Input(0), new Weight(weightsNode.get(i + 1).floatValue()));
             }
 
-            Neuron n = new Neuron(id, synapses, bias, new TanH());
+            Neuron n = new Neuron(id, synapses, bias, func);
             neuronsByLayer.computeIfAbsent(layerIndex, k -> new ArrayList<>()).add(n);
         });