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.abstraction.TrainingStep;
 import com.naaturel.ANN.domain.model.dataset.DataSet;
 import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
@@ -11,6 +12,7 @@ import com.naaturel.ANN.domain.model.training.TrainingPipeline;
 import com.naaturel.ANN.implementation.activation.Heaviside;
 import com.naaturel.ANN.implementation.correction.SimpleCorrectionStrategy;
 import com.naaturel.ANN.implementation.neuron.SimplePerceptron;
+import com.naaturel.ANN.implementation.training.SimpleTraining;
 import com.naaturel.ANN.implementation.training.steps.*;
 
 import java.util.*;
@@ -67,25 +69,8 @@ public class Main {
         Layer layer = new Layer(List.of(neuron));
         Network network = new Network(List.of(layer));
 
-        TrainingContext context = new TrainingContext();
+        Trainer trainer = new SimpleTraining();
-        context.dataset = orDataSet;
+        trainer.train(network, orDataSet);
-        context.model = network;
-        context.learningRate = 0.3F;
-
-        List<TrainingStep> steps = List.of(
-                new PredictionStep(),
-                new DeltaStep(),
-                new SimpleLossStep(),
-                new SimpleErrorDetectionStep(),
-                new WeightCorrectionStep(new SimpleCorrectionStrategy())
-        );
-
-        TrainingPipeline pipeline = new TrainingPipeline(steps);
-        pipeline
-                .stopCondition(ctx -> ctx.globalLoss == 0.0F || ctx.epoch > 100)
-                .beforeEpoch(ctx -> ctx.globalLoss = 0)
-                .withVerbose(true)
-                .run(context);
 
     }
 }

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

@@ -1,10 +1,7 @@
 package com.naaturel.ANN.domain.abstraction;
 
-import com.naaturel.ANN.domain.model.training.TrainingContext;
+import com.naaturel.ANN.domain.model.dataset.DataSet;
-
-import java.util.List;
 
 public interface Trainer {
-
+    void train(Trainable model, DataSet dataset);
-    void train(TrainingContext context, List<TrainingStep> steps);
 }

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

@@ -1,9 +1,9 @@
 package com.naaturel.ANN.implementation.training;
 
+import com.naaturel.ANN.domain.abstraction.Trainable;
 import com.naaturel.ANN.domain.abstraction.Trainer;
 import com.naaturel.ANN.domain.abstraction.TrainingStep;
 import com.naaturel.ANN.domain.model.dataset.DataSet;
-import com.naaturel.ANN.domain.model.neuron.Network;
 import com.naaturel.ANN.domain.model.training.TrainingContext;
 import com.naaturel.ANN.domain.model.training.TrainingPipeline;
 import com.naaturel.ANN.implementation.correction.SimpleCorrectionStrategy;
@@ -18,8 +18,26 @@ public class SimpleTraining implements Trainer {
     }
 
     @Override
-    public void train(TrainingContext context, List<TrainingStep> steps) {
+    public void train(Trainable model, DataSet dataset) {
+        TrainingContext context = new TrainingContext();
+        context.dataset = dataset;
+        context.model = model;
+        context.learningRate = 0.3F;
 
+        List<TrainingStep> steps = List.of(
+                new PredictionStep(),
+                new DeltaStep(),
+                new SimpleLossStep(),
+                new SimpleErrorDetectionStep(),
+                new WeightCorrectionStep(new SimpleCorrectionStrategy())
+        );
+
+        TrainingPipeline pipeline = new TrainingPipeline(steps);
+        pipeline
+                .stopCondition(ctx -> ctx.globalLoss == 0.0F || ctx.epoch > 100)
+                .beforeEpoch(ctx -> ctx.globalLoss = 0)
+                .withVerbose(true)
+                .run(context);
     }
 
     /*public void train(Neuron n, float learningRate, DataSet dataSet) {