Implement model selector and fix tests

build.gradle.kts

@@ -5,6 +5,8 @@ plugins {
 group = "be.naaturel"
 version = "1.0-SNAPSHOT"
 
+
+
 repositories {
     mavenCentral()
 }
@@ -13,11 +15,21 @@ dependencies {
     implementation("org.jfree:jfreechart:1.5.4")
     implementation("com.fasterxml.jackson.core:jackson-databind:2.21.2")
 
+    implementation("org.jline:jline:3.27.1")
+
     testImplementation(platform("org.junit:junit-bom:5.10.0"))
     testImplementation("org.junit.jupiter:junit-jupiter")
     testRuntimeOnly("org.junit.platform:junit-platform-launcher")
 }
 
+tasks.jar {
+    manifest {
+        attributes["Main-Class"] = "com.naaturel.ANN.Main"
+    }
+    from(configurations.runtimeClasspath.get().map { if (it.isDirectory) it else zipTree(it) })
+    duplicatesStrategy = DuplicatesStrategy.EXCLUDE
+}
+
 tasks.test {
     useJUnitPlatform()
 }

config.json

@@ -1,14 +1,14 @@
 {
   "model": {
     "new": true,
-    "parameters": [5, 5, 1],
+    "parameters": [1],
-    "path": "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/snapshots/snapshot-4-12.json"
+    "path": "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/snapshots/snapshot-test.json"
   },
   "training" : {
     "learning_rate" : 0.03,
     "max_epoch" : 5000
   },
   "dataset" : {
-    "path" : "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/xor.csv"
+    "path" : "C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and.csv"
   }
 }

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

@@ -1,16 +1,16 @@
 package com.naaturel.ANN;
 
 import com.naaturel.ANN.domain.abstraction.Model;
-import com.naaturel.ANN.domain.model.neuron.Neuron;
 import com.naaturel.ANN.domain.abstraction.Trainer;
-import com.naaturel.ANN.implementation.multiLayers.TanH;
+import com.naaturel.ANN.implementation.training.AdalineTraining;
 import com.naaturel.ANN.implementation.training.GradientBackpropagationTraining;
+import com.naaturel.ANN.implementation.training.GradientDescentTraining;
+import com.naaturel.ANN.implementation.training.SimpleTraining;
 import com.naaturel.ANN.infrastructure.config.ConfigDto;
 import com.naaturel.ANN.infrastructure.config.ConfigLoader;
 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.visualization.GraphVisualizer;
 import com.naaturel.ANN.infrastructure.persistence.ModelSnapshot;
 import com.naaturel.ANN.infrastructure.visualization.ModelVisualizer;
@@ -21,7 +21,17 @@ public class Main {
 
     public static void main(String[] args) throws Exception {
 
+        String[] types = {"Simple perceptron", "Gradient descent", "Adaline", "Gradient retro-propagation"};
+
         Scanner sc = new Scanner(System.in);
+        for (int i = 0; i < types.length; i++) {
+            System.out.printf("%d - %s\n", i+1, types[i]);
+        }
+
+        System.out.print(">>> ");
+        int typeIndex = sc.nextInt() - 1;
+        sc.nextLine();
+        System.out.printf("\nChosen type: %s\n", types[typeIndex]);
 
         ConfigDto config = ConfigLoader.load("C:/Users/Laurent/Desktop/ANN-framework/config.json");
 
@@ -43,7 +53,13 @@ public class Main {
         ModelSnapshot snapshot = new ModelSnapshot();
 
         if(newModel) {
-            Trainer trainer = new GradientBackpropagationTraining(modelParameters, nbrInput);
+            Trainer trainer = switch (typeIndex) {
+                case 0 -> new SimpleTraining(modelParameters, nbrInput);
+                case 1 -> new GradientDescentTraining(modelParameters, nbrInput);
+                case 2 -> new AdalineTraining(modelParameters, nbrInput);
+                case 3 -> new GradientBackpropagationTraining(modelParameters, nbrInput);
+                default -> throw new IllegalStateException("Unexpected value: " + typeIndex);
+            };
             trainer.train(learningRate, maxEpoch, dataset);
             trainer.saveModel(snapshot, modelPath);
         }
@@ -56,8 +72,6 @@ public class Main {
                 .display();
     }
 
-
-
     private static void plotGraph(DataSet dataset, Model network){
 
         if(dataset.getNbrInputs() != 2) return;

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

@@ -1,15 +1,15 @@
 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.multiLayers.TanH;
 
 import java.util.ArrayList;
 import java.util.List;
 
 public class ModelCreator {
 
-    public static Model createModel(int[] neuronPerLayer, int nbrInput){
+    public static Model createModel(int[] neuronPerLayer, int nbrInput, ActivationFunction func){
         int neuronId = 0;
         List<Layer> layers = new ArrayList<>();
         for (int i = 0; i < neuronPerLayer.length; i++){
@@ -26,7 +26,7 @@ public class ModelCreator {
 
                 Bias bias =  new Bias(new Weight());
 
-                Neuron n = new Neuron(neuronId, syns.toArray(new Synapse[0]), bias, new TanH());
+                Neuron n = new Neuron(neuronId, syns.toArray(new Synapse[0]), bias, func);
                 neurons.add(n);
                 neuronId++;
             }

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

@@ -4,6 +4,7 @@ import com.naaturel.ANN.domain.abstraction.AlgorithmStep;
 import com.naaturel.ANN.domain.abstraction.Model;
 import com.naaturel.ANN.domain.abstraction.Trainer;
 import com.naaturel.ANN.domain.model.helpers.ModelCreator;
+import com.naaturel.ANN.implementation.gradientDescent.Linear;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
 import com.naaturel.ANN.domain.model.training.TrainingPipeline;
 import com.naaturel.ANN.implementation.adaline.AdalineTrainingContext;
@@ -23,7 +24,7 @@ public class AdalineTraining implements Trainer {
     private Model model;
 
     public AdalineTraining(int[] neurons, int nbrInputs){
-        model = ModelCreator.createModel(neurons, nbrInputs);
+        model = ModelCreator.createModel(neurons, nbrInputs, new Linear(1, 0));
     }
 
     @Override

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

@@ -18,7 +18,7 @@ public class GradientBackpropagationTraining implements Trainer {
     private Model model;
 
     public GradientBackpropagationTraining(int[] neurons, int nbrInputs){
-        model = ModelCreator.createModel(neurons, nbrInputs);
+        model = ModelCreator.createModel(neurons, nbrInputs, new TanH());
     }
 
     public Model getModel() {

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

@@ -4,12 +4,9 @@ import com.naaturel.ANN.domain.abstraction.AlgorithmStep;
 import com.naaturel.ANN.domain.abstraction.Model;
 import com.naaturel.ANN.domain.abstraction.Trainer;
 import com.naaturel.ANN.domain.model.helpers.ModelCreator;
+import com.naaturel.ANN.implementation.gradientDescent.*;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
-import com.naaturel.ANN.implementation.gradientDescent.GradientDescentErrorStrategy;
-import com.naaturel.ANN.implementation.gradientDescent.GradientDescentTrainingContext;
 import com.naaturel.ANN.domain.model.training.TrainingPipeline;
-import com.naaturel.ANN.implementation.gradientDescent.GradientDescentCorrectionStrategy;
-import com.naaturel.ANN.implementation.gradientDescent.SquareLossStep;
 import com.naaturel.ANN.implementation.simplePerceptron.SimpleDeltaStep;
 import com.naaturel.ANN.implementation.simplePerceptron.SimplePredictionStep;
 import com.naaturel.ANN.infrastructure.persistence.ModelSnapshot;
@@ -23,7 +20,7 @@ public class GradientDescentTraining implements Trainer {
     private Model model;
 
     public GradientDescentTraining(int[] neurons, int nbrInputs){
-        model = ModelCreator.createModel(neurons, nbrInputs);
+        model = ModelCreator.createModel(neurons, nbrInputs, new Linear(1, 0));
     }
 
     @Override

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);
+        model = ModelCreator.createModel(neurons, nbrInputs, new Heaviside());
     }
 
     @Override

src/test/java/adaline/AdalineTest.java

@@ -1,8 +1,10 @@
 package adaline;
 
 
+import com.naaturel.ANN.domain.abstraction.AlgorithmStep;
+import com.naaturel.ANN.domain.abstraction.Model;
+import com.naaturel.ANN.domain.model.helpers.ModelCreator;
 import com.naaturel.ANN.domain.model.neuron.Neuron;
-import com.naaturel.ANN.domain.abstraction.TrainingStep;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
 import com.naaturel.ANN.infrastructure.dataset.DatasetExtractor;
 import com.naaturel.ANN.domain.model.neuron.*;
@@ -13,7 +15,6 @@ import com.naaturel.ANN.implementation.simplePerceptron.SimpleCorrectionStep;
 import com.naaturel.ANN.implementation.simplePerceptron.SimpleDeltaStep;
 import com.naaturel.ANN.implementation.simplePerceptron.SimpleErrorRegistrationStep;
 import com.naaturel.ANN.implementation.simplePerceptron.SimplePredictionStep;
-import com.naaturel.ANN.implementation.training.steps.*;
 import org.junit.jupiter.api.BeforeEach;
 import org.junit.jupiter.api.Test;
 
@@ -27,9 +28,7 @@ public class AdalineTest {
     private DataSet dataset;
     private AdalineTrainingContext context;
 
-    private List<Synapse> synapses;
+    private Model model;
-    private Bias bias;
-    private FullyConnectedNetwork network;
 
     private TrainingPipeline pipeline;
 
@@ -42,22 +41,16 @@ public class AdalineTest {
         syns.add(new Synapse(new Input(0), new Weight(0)));
         syns.add(new Synapse(new Input(0), new Weight(0)));
 
-        bias =  new Bias(new Weight(0));
+        model = ModelCreator.createModel(new int[]{1}, 2, new Linear(1, 0));
 
-        Neuron neuron = new Neuron(syns, bias, new Linear(1, 0));
+        context = new AdalineTrainingContext(model, dataset);
-        Layer layer = new Layer(List.of(neuron));
-        network = new FullyConnectedNetwork(List.of(layer));
 
-        context = new AdalineTrainingContext();
+        List<AlgorithmStep> steps = List.of(
-        context.dataset = dataset;
+                new SimplePredictionStep(context),
-        context.model = network;
+                new SimpleDeltaStep(context),
-
+                new SquareLossStep(context),
-        List<TrainingStep> steps = List.of(
+                new SimpleErrorRegistrationStep(context),
-                new PredictionStep(new SimplePredictionStep(context)),
+                new SimpleCorrectionStep(context)
-                new DeltaStep(new SimpleDeltaStep(context)),
-                new LossStep(new SquareLossStep(context)),
-                new ErrorRegistrationStep(new SimpleErrorRegistrationStep(context)),
-                new WeightCorrectionStep(new SimpleCorrectionStep(context))
         );
 
         pipeline = new TrainingPipeline(steps)

src/test/java/gradientDescent/GradientDescentTest.java

@@ -1,14 +1,14 @@
 package gradientDescent;
 
-import com.naaturel.ANN.domain.model.neuron.Neuron;
+import com.naaturel.ANN.domain.abstraction.AlgorithmStep;
-import com.naaturel.ANN.domain.abstraction.TrainingStep;
+import com.naaturel.ANN.domain.abstraction.Model;
+import com.naaturel.ANN.domain.model.helpers.ModelCreator;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
 import com.naaturel.ANN.infrastructure.dataset.DatasetExtractor;
 import com.naaturel.ANN.domain.model.neuron.*;
 import com.naaturel.ANN.domain.model.training.TrainingPipeline;
 import com.naaturel.ANN.implementation.gradientDescent.*;
 import com.naaturel.ANN.implementation.simplePerceptron.*;
-import com.naaturel.ANN.implementation.training.steps.*;
 import org.junit.jupiter.api.BeforeEach;
 import org.junit.jupiter.api.Test;
 
@@ -23,9 +23,7 @@ public class GradientDescentTest {
     private DataSet dataset;
     private GradientDescentTrainingContext context;
 
-    private List<Synapse> synapses;
+    private Model model;
-    private Bias bias;
-    private FullyConnectedNetwork network;
 
     private TrainingPipeline pipeline;
 
@@ -34,26 +32,16 @@ public class GradientDescentTest {
         dataset = new DatasetExtractor()
                 .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and-gradient.csv", 1);
 
-        List<Synapse> syns = new ArrayList<>();
+        model = ModelCreator.createModel(new int[]{1}, 2, new Linear(1, 0));
-        syns.add(new Synapse(new Input(0), new Weight(0)));
-        syns.add(new Synapse(new Input(0), new Weight(0)));
 
-        bias =  new Bias(new Weight(0));
+        context = new GradientDescentTrainingContext(model, dataset);
-
-        Neuron neuron = new Neuron(syns, bias, new Linear(1, 0));
-        Layer layer = new Layer(List.of(neuron));
-        network = new FullyConnectedNetwork(List.of(layer));
-
-        context = new GradientDescentTrainingContext();
-        context.dataset = dataset;
-        context.model = network;
         context.correctorTerms = new ArrayList<>();
 
-        List<TrainingStep> steps = List.of(
+        List<AlgorithmStep> steps = List.of(
-                new PredictionStep(new SimplePredictionStep(context)),
+                new SimplePredictionStep(context),
-                new DeltaStep(new SimpleDeltaStep(context)),
+                new SimpleDeltaStep(context),
-                new LossStep(new SquareLossStep(context)),
+                new SquareLossStep(context),
-                new ErrorRegistrationStep(new GradientDescentErrorStrategy(context))
+                new GradientDescentErrorStrategy(context)
         );
 
         pipeline = new TrainingPipeline(steps)
@@ -91,7 +79,7 @@ public class GradientDescentTest {
         });
 
         pipeline
-                .withVerbose(true)
+                .withVerbose(true, 1)
                 .run(context);
         assertEquals(67, context.epoch);
     }

src/test/java/perceptron/SimplePerceptronTest.java

@@ -1,13 +1,13 @@
 package perceptron;
 
-import com.naaturel.ANN.domain.model.neuron.Neuron;
+import com.naaturel.ANN.domain.abstraction.AlgorithmStep;
-import com.naaturel.ANN.domain.abstraction.TrainingStep;
+import com.naaturel.ANN.domain.abstraction.Model;
+import com.naaturel.ANN.domain.model.helpers.ModelCreator;
 import com.naaturel.ANN.infrastructure.dataset.DataSet;
 import com.naaturel.ANN.infrastructure.dataset.DatasetExtractor;
 import com.naaturel.ANN.domain.model.neuron.*;
 import com.naaturel.ANN.domain.model.training.TrainingPipeline;
 import com.naaturel.ANN.implementation.simplePerceptron.*;
-import com.naaturel.ANN.implementation.training.steps.*;
 import org.junit.jupiter.api.BeforeEach;
 import org.junit.jupiter.api.Test;
 
@@ -21,10 +21,7 @@ public class SimplePerceptronTest {
 
     private DataSet dataset;
     private SimpleTrainingContext context;
-
+    private Model model;
-    private List<Synapse> synapses;
-    private Bias bias;
-    private FullyConnectedNetwork network;
 
     private TrainingPipeline pipeline;
 
@@ -37,22 +34,16 @@ public class SimplePerceptronTest {
         syns.add(new Synapse(new Input(0), new Weight(0)));
         syns.add(new Synapse(new Input(0), new Weight(0)));
 
-        bias =  new Bias(new Weight(0));
+        model = ModelCreator.createModel(new int[]{1}, 2, new Heaviside());
 
-        Neuron neuron = new Neuron(syns, bias, new Heaviside());
+        context = new SimpleTrainingContext(model, dataset);
-        Layer layer = new Layer(List.of(neuron));
-        network = new FullyConnectedNetwork(List.of(layer));
 
-        context = new SimpleTrainingContext();
+        List<AlgorithmStep> steps = List.of(
-        context.dataset = dataset;
+                new SimplePredictionStep(context),
-        context.model = network;
+                new SimpleDeltaStep(context),
-
+                new SimpleLossStrategy(context),
-        List<TrainingStep> steps = List.of(
+                new SimpleErrorRegistrationStep(context),
-                new PredictionStep(new SimplePredictionStep(context)),
+                new SimpleCorrectionStep(context)
-                new DeltaStep(new SimpleDeltaStep(context)),
-                new LossStep(new SimpleLossStrategy(context)),
-                new ErrorRegistrationStep(new SimpleErrorRegistrationStep(context)),
-                new WeightCorrectionStep(new SimpleCorrectionStep(context))
         );
 
         pipeline = new TrainingPipeline(steps);
@@ -74,7 +65,7 @@ public class SimplePerceptronTest {
 
         context.learningRate = 1F;
         pipeline.afterEpoch(ctx -> {
-            int index = ctx.epoch-1;
+            int index = ctx.epoch;
             assertEquals(expectedGlobalLosses.get(index), context.globalLoss);
         });