Fix implementation

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

@@ -2,28 +2,26 @@ 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;
 import com.naaturel.ANN.domain.model.dataset.DatasetExtractor;
-import com.naaturel.ANN.domain.model.dataset.Label;
 import com.naaturel.ANN.domain.model.neuron.*;
-import com.naaturel.ANN.domain.model.training.TrainingContext;
+import com.naaturel.ANN.implementation.gradientDescent.Linear;
-import com.naaturel.ANN.domain.model.training.TrainingPipeline;
+import com.naaturel.ANN.implementation.simplePerceptron.Heaviside;
-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.GradientDescentTraining;
 import com.naaturel.ANN.implementation.training.SimpleTraining;
-import com.naaturel.ANN.implementation.training.steps.*;
 
-import javax.xml.crypto.Data;
 import java.util.*;
 
 public class Main {
 
     public static void main(String[] args){
 
-        DataSet dataset = new DatasetExtractor().extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/or.csv");
+        DataSet dataset = new DatasetExtractor()
+                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/table_2_9.csv");
+
+        DataSet orDataset = new DatasetExtractor()
+                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/or.csv");
 
         List<Synapse> syns = new ArrayList<>();
         syns.add(new Synapse(new Input(0), new Weight(0)));
@@ -31,11 +29,11 @@ public class Main {
 
         Bias bias =  new Bias(new Weight(0));
 
-        Neuron neuron = new SimplePerceptron(syns, bias, new Heaviside());
+        Neuron neuron = new SimplePerceptron(syns, bias, new Linear());
         Layer layer = new Layer(List.of(neuron));
         Network network = new Network(List.of(layer));
 
-        Trainer trainer = new SimpleTraining();
+        Trainer trainer = new GradientDescentTraining();
         trainer.train(network, dataset);
 
     }

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

@@ -1,9 +1,7 @@
 package com.naaturel.ANN.domain.abstraction;
 
-import com.naaturel.ANN.domain.model.training.TrainingContext;
-
 public interface AlgorithmStrategy {
 
-    void apply(TrainingContext ctx);
+    void apply();
 
 }

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

@@ -6,9 +6,9 @@ import com.naaturel.ANN.domain.model.neuron.Synapse;
 import java.util.List;
 import java.util.function.Consumer;
 
-public interface Trainable {
+public interface Model {
+    int synCount();
+    void applyOnSynapses(Consumer<Synapse> consumer);
     List<Float> predict(List<Input> inputs);
 
-    void applyOnSynapses(Consumer<Synapse> consumer);
-
 }

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

@@ -4,10 +4,9 @@ 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 abstract class Neuron implements Trainable {
+public abstract class Neuron implements Model {
 
     protected List<Synapse> synapses;
     protected Bias bias;
@@ -35,4 +34,9 @@ public abstract class Neuron implements Trainable {
             syn.setInput(inputs.get(i));
         }
     }
+
+    @Override
+    public int synCount() {
+        return this.synapses.size()+1; //take the bias in account
+    }
 }

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

@@ -3,5 +3,5 @@ package com.naaturel.ANN.domain.abstraction;
 import com.naaturel.ANN.domain.model.dataset.DataSet;
 
 public interface Trainer {
-    void train(Trainable model, DataSet dataset);
+    void train(Model model, DataSet dataset);
 }

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

@@ -1,21 +1,21 @@
-package com.naaturel.ANN.domain.model.training;
+package com.naaturel.ANN.domain.abstraction;
 
-import com.naaturel.ANN.domain.abstraction.Trainable;
 import com.naaturel.ANN.domain.model.dataset.DataSet;
 import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
 import com.naaturel.ANN.domain.model.dataset.Label;
 
-public class TrainingContext {
+public abstract class TrainingContext {
-    public Trainable model;
+    public Model model;
     public DataSet dataset;
     public DataSetEntry currentEntry;
-    public Label currentLabel;
 
+    public Label currentLabel;
     public float prediction;
     public float delta;
-    public float localLoss;
-    public float globalLoss;
-    public float learningRate;
 
+    public float globalLoss;
+    public float localLoss;
+
+    public float learningRate;
     public int epoch;
 }

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

@@ -1,9 +1,7 @@
 package com.naaturel.ANN.domain.abstraction;
 
-import com.naaturel.ANN.domain.model.training.TrainingContext;
-
 public interface TrainingStep {
 
-    void run(TrainingContext ctx);
+    void run();
 
 }

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

@@ -1,13 +1,13 @@
 package com.naaturel.ANN.domain.model.neuron;
 
 import com.naaturel.ANN.domain.abstraction.Neuron;
-import com.naaturel.ANN.domain.abstraction.Trainable;
+import com.naaturel.ANN.domain.abstraction.Model;
 
 import java.util.ArrayList;
 import java.util.List;
 import java.util.function.Consumer;
 
-public class Layer implements Trainable {
+public class Layer implements Model {
 
     private final List<Neuron> neurons;
 
@@ -25,6 +25,15 @@ public class Layer implements Trainable {
         return result;
     }
 
+    @Override
+    public int synCount() {
+        int res = 0;
+        for (Neuron neuron : this.neurons) {
+            res += neuron.synCount();
+        }
+        return res;
+    }
+
     @Override
     public void applyOnSynapses(Consumer<Synapse> consumer) {
         this.neurons.forEach(neuron -> neuron.applyOnSynapses(consumer));

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

@@ -1,12 +1,12 @@
 package com.naaturel.ANN.domain.model.neuron;
 
-import com.naaturel.ANN.domain.abstraction.Trainable;
+import com.naaturel.ANN.domain.abstraction.Model;
 
 import java.util.ArrayList;
 import java.util.List;
 import java.util.function.Consumer;
 
-public class Network implements Trainable {
+public class Network implements Model {
 
     private final List<Layer> layers;
 
@@ -24,6 +24,15 @@ public class Network implements Trainable {
         return result;
     }
 
+    @Override
+    public int synCount() {
+        int res = 0;
+        for(Layer layer : this.layers){
+            res += layer.synCount();
+        }
+        return res;
+    }
+
     @Override
     public void applyOnSynapses(Consumer<Synapse> consumer) {
         this.layers.forEach(layer -> layer.applyOnSynapses(consumer));

src/main/java/com/naaturel/ANN/domain/model/training/TrainingPipeline.java

@@ -1,5 +1,6 @@
 package com.naaturel.ANN.domain.model.training;
 
+import com.naaturel.ANN.domain.abstraction.TrainingContext;
 import com.naaturel.ANN.domain.abstraction.TrainingStep;
 import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
 
@@ -55,6 +56,9 @@ public class TrainingPipeline {
             this.beforeEpoch.accept(ctx);
             this.executeSteps(ctx);
             this.afterEpoch.accept(ctx);
+            if(this.verbose) {
+                System.out.printf("[Global error] : %.2f\n", ctx.globalLoss);
+            }
         } while (!this.stopCondition.test(ctx));
     }
 
@@ -63,18 +67,16 @@ public class TrainingPipeline {
             ctx.currentEntry = entry;
             ctx.currentLabel = ctx.dataset.getLabel(entry);
             for (TrainingStep step : steps) {
-                step.run(ctx);
+                step.run();
             }
             if(this.verbose) {
                 System.out.printf("Epoch : %d, ", ctx.epoch);
                 System.out.printf("predicted : %.2f, ", ctx.prediction);
                 System.out.printf("expected : %.2f, ", ctx.currentLabel.getValue());
-                System.out.printf("delta : %.2f\n", ctx.delta);
+                System.out.printf("delta : %.2f, ", ctx.delta);
+                System.out.printf("loss : %.5f\n", ctx.localLoss);
             }
         }
-        if(this.verbose) {
-            System.out.printf("[Global error] : %.2f\n", ctx.globalLoss);
-        }
         ctx.epoch += 1;
     }
 }

src/main/java/com/naaturel/ANN/implementation/correction/GradientDescentCorrectionStrategy.java

@@ -1,25 +0,0 @@
-package com.naaturel.ANN.implementation.correction;
-
-import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
-import com.naaturel.ANN.domain.model.training.TrainingContext;
-
-import java.util.ArrayList;
-import java.util.List;
-
-public class GradientDescentCorrectionStrategy implements AlgorithmStrategy {
-
-    List<Float> correctorTerms;
-
-    public GradientDescentCorrectionStrategy(int nbrCorrectors){
-        this.correctorTerms = new ArrayList<>();
-        for (int i = 0; i < nbrCorrectors; i++){
-            correctorTerms.add(0F);
-        }
-    }
-
-    @Override
-    public void apply(TrainingContext context) {
-
-    }
-
-}

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

@@ -0,0 +1,25 @@
+package com.naaturel.ANN.implementation.gradientDescent;
+
+import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+
+import java.util.concurrent.atomic.AtomicInteger;
+
+public class GradientDescentCorrectionStrategy implements AlgorithmStrategy {
+
+    private final GradientDescentTrainingContext context;
+
+    public GradientDescentCorrectionStrategy(GradientDescentTrainingContext context) {
+        this.context = context;
+    }
+
+    @Override
+    public void apply() {
+        AtomicInteger i = new AtomicInteger(0);
+        context.model.applyOnSynapses(syn -> {
+            float corrector = context.correctorTerms.get(i.get());
+            float c = syn.getWeight() + corrector;
+            syn.setWeight(c);
+            i.incrementAndGet();
+        });
+    }
+}

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

@@ -0,0 +1,26 @@
+package com.naaturel.ANN.implementation.gradientDescent;
+
+import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+
+import java.util.concurrent.atomic.AtomicInteger;
+
+public class GradientDescentErrorStrategy implements AlgorithmStrategy {
+
+    private final GradientDescentTrainingContext context;
+
+    public GradientDescentErrorStrategy(GradientDescentTrainingContext context) {
+        this.context = context;
+    }
+
+
+    @Override
+    public void apply() {
+        AtomicInteger i = new AtomicInteger(0);
+        context.model.applyOnSynapses(syn -> {
+            float corrector = context.correctorTerms.get(i.get());
+            corrector += context.learningRate * context.delta * syn.getInput();
+            context.correctorTerms.set(i.get(), corrector);
+            i.incrementAndGet();
+        });
+    }
+}

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

@@ -0,0 +1,11 @@
+package com.naaturel.ANN.implementation.gradientDescent;
+
+import com.naaturel.ANN.domain.abstraction.TrainingContext;
+
+import java.util.List;
+
+public class GradientDescentTrainingContext extends TrainingContext {
+
+    public List<Float> correctorTerms;
+
+}

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

@@ -1,4 +1,4 @@
-package com.naaturel.ANN.implementation.activation;
+package com.naaturel.ANN.implementation.gradientDescent;
 
 import com.naaturel.ANN.domain.abstraction.ActivationFunction;
 import com.naaturel.ANN.domain.abstraction.Neuron;

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

@@ -0,0 +1,19 @@
+package com.naaturel.ANN.implementation.gradientDescent;
+
+import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+import com.naaturel.ANN.implementation.simplePerceptron.SimpleTrainingContext;
+
+public class SquareLossStrategy implements AlgorithmStrategy {
+
+    private final GradientDescentTrainingContext context;
+
+    public SquareLossStrategy(GradientDescentTrainingContext context) {
+        this.context = context;
+    }
+
+    @Override
+    public void apply() {
+        this.context.localLoss = (float)Math.pow(this.context.delta, 2)/2;
+        this.context.globalLoss += context.localLoss;
+    }
+}

src/main/java/com/naaturel/ANN/implementation/loss/SimpleLossStrategy.java

@@ -1,11 +0,0 @@
-package com.naaturel.ANN.implementation.loss;
-
-import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
-import com.naaturel.ANN.domain.model.training.TrainingContext;
-
-public class SimpleLossStrategy implements AlgorithmStrategy {
-    @Override
-    public void apply(TrainingContext ctx) {
-        ctx.localLoss = Math.abs(ctx.delta);
-    }
-}

src/main/java/com/naaturel/ANN/implementation/loss/SquareLossStrategy.java

@@ -1,11 +0,0 @@
-package com.naaturel.ANN.implementation.loss;
-
-import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
-import com.naaturel.ANN.domain.model.training.TrainingContext;
-
-public class SquareLossStrategy implements AlgorithmStrategy {
-    @Override
-    public void apply(TrainingContext ctx) {
-        ctx.localLoss = (float)Math.pow(ctx.delta, 2) / 2;
-    }
-}

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

@@ -1,4 +1,4 @@
-package com.naaturel.ANN.implementation.activation;
+package com.naaturel.ANN.implementation.simplePerceptron;
 
 import com.naaturel.ANN.domain.abstraction.ActivationFunction;
 import com.naaturel.ANN.domain.abstraction.Neuron;

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

@@ -1,14 +1,18 @@
-package com.naaturel.ANN.implementation.correction;
+package com.naaturel.ANN.implementation.simplePerceptron;
 
 import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
-import com.naaturel.ANN.domain.model.training.TrainingContext;
 
 public class SimpleCorrectionStrategy implements AlgorithmStrategy {
 
-    @Override
+    private final SimpleTrainingContext context;
-    public void apply(TrainingContext context) {
-        if(context.currentLabel.getValue() == context.prediction) return ;
 
+    public SimpleCorrectionStrategy(SimpleTrainingContext context) {
+        this.context = context;
+    }
+
+    @Override
+    public void apply() {
+        if(context.currentLabel.getValue() == context.prediction) return ;
         context.model.applyOnSynapses(syn -> {
             float currentW = syn.getWeight();
             float currentInput  = syn.getInput();

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

@@ -0,0 +1,26 @@
+package com.naaturel.ANN.implementation.simplePerceptron;
+
+import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+import com.naaturel.ANN.domain.abstraction.TrainingContext;
+import com.naaturel.ANN.domain.model.dataset.DataSet;
+import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
+import com.naaturel.ANN.domain.model.dataset.Label;
+
+public class SimpleDeltaStrategy implements AlgorithmStrategy {
+
+    private final TrainingContext context;
+
+    public SimpleDeltaStrategy(TrainingContext context) {
+        this.context = context;
+    }
+
+    @Override
+    public void apply() {
+        DataSet dataSet = context.dataset;
+        DataSetEntry entry = context.currentEntry;
+        Label label = dataSet.getLabel(entry);
+
+        context.delta = label.getValue() - context.prediction;
+    }
+
+}

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

@@ -0,0 +1,17 @@
+package com.naaturel.ANN.implementation.simplePerceptron;
+
+import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+
+public class SimpleErrorRegistrationStrategy implements AlgorithmStrategy {
+
+    private final SimpleTrainingContext context;
+
+    public SimpleErrorRegistrationStrategy(SimpleTrainingContext context) {
+        this.context = context;
+    }
+
+    @Override
+    public void apply() {
+        context.globalLoss += context.localLoss;
+    }
+}

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

@@ -0,0 +1,17 @@
+package com.naaturel.ANN.implementation.simplePerceptron;
+
+import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+
+public class SimpleLossStrategy implements AlgorithmStrategy {
+
+    private final SimpleTrainingContext context;
+
+    public SimpleLossStrategy(SimpleTrainingContext context) {
+        this.context = context;
+    }
+
+    @Override
+    public void apply() {
+        this.context.localLoss = Math.abs(this.context.delta);
+    }
+}

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

@@ -0,0 +1,21 @@
+package com.naaturel.ANN.implementation.simplePerceptron;
+
+import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+import com.naaturel.ANN.domain.abstraction.TrainingContext;
+
+import java.util.List;
+
+public class SimplePredictionStrategy implements AlgorithmStrategy {
+
+    private final TrainingContext context;
+
+    public SimplePredictionStrategy(TrainingContext context) {
+        this.context = context;
+    }
+
+    @Override
+    public void apply() {
+        List<Float> predictions = context.model.predict(context.currentEntry.getData());
+        context.prediction = predictions.getFirst();
+    }
+}

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

@@ -0,0 +1,6 @@
+package com.naaturel.ANN.implementation.simplePerceptron;
+
+import com.naaturel.ANN.domain.abstraction.TrainingContext;
+
+public class SimpleTrainingContext extends TrainingContext {
+}

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

@@ -1,16 +1,19 @@
 package com.naaturel.ANN.implementation.training;
 
-import com.naaturel.ANN.domain.abstraction.Trainable;
+import com.naaturel.ANN.domain.abstraction.Model;
 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.training.TrainingContext;
+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.correction.GradientDescentCorrectionStrategy;
+import com.naaturel.ANN.implementation.gradientDescent.GradientDescentCorrectionStrategy;
-import com.naaturel.ANN.implementation.correction.SimpleCorrectionStrategy;
+import com.naaturel.ANN.implementation.gradientDescent.SquareLossStrategy;
-import com.naaturel.ANN.implementation.loss.SquareLossStrategy;
+import com.naaturel.ANN.implementation.simplePerceptron.SimpleDeltaStrategy;
+import com.naaturel.ANN.implementation.simplePerceptron.SimplePredictionStrategy;
 import com.naaturel.ANN.implementation.training.steps.*;
 
+import java.util.ArrayList;
 import java.util.List;
 
 public class GradientDescentTraining implements Trainer {
@@ -20,25 +23,31 @@ public class GradientDescentTraining implements Trainer {
     }
 
     @Override
-    public void train(Trainable model, DataSet dataset) {
+    public void train(Model model, DataSet dataset) {
-        TrainingContext context = new TrainingContext();
+        GradientDescentTrainingContext context = new GradientDescentTrainingContext();
         context.dataset = dataset;
         context.model = model;
-        context.learningRate = 0.3F;
+        context.learningRate = 0.00011F;
+        context.correctorTerms =  new ArrayList<>();
 
         List<TrainingStep> steps = List.of(
-                new PredictionStep(),
+                new PredictionStep(new SimplePredictionStrategy(context)),
-                new DeltaStep(),
+                new DeltaStep(new SimpleDeltaStrategy(context)),
-                new LossStep(new SquareLossStrategy()),
+                new LossStep(new SquareLossStrategy(context)),
-                new SimpleErrorDetectionStep(),
+                new ErrorRegistrationStep(new GradientDescentErrorStrategy(context)),
-                new WeightCorrectionStep(new GradientDescentCorrectionStrategy(2))
+                new WeightCorrectionStep(new GradientDescentCorrectionStrategy(context))
         );
 
         TrainingPipeline pipeline = new TrainingPipeline(steps);
         pipeline
-                .stopCondition(ctx -> ctx.globalLoss == 0.0F || ctx.epoch > 100)
+                .stopCondition(ctx -> ctx.globalLoss == 0.0F || ctx.epoch > 1000)
-                .beforeEpoch(ctx -> ctx.globalLoss = 0)
+                .beforeEpoch(ctx -> {
-                .afterEpoch(ctx -> ())
+                    ctx.globalLoss = 0.0F;
+                    for (int i = 0; i < model.synCount(); i++){
+                        context.correctorTerms.add(0F);
+                    }
+                })
+                .afterEpoch(ctx -> ctx.globalLoss /= ctx.dataset.size())
                 .withVerbose(true)
                 .withTimeMeasurement(true)
                 .run(context);

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

@@ -1,13 +1,11 @@
 package com.naaturel.ANN.implementation.training;
 
-import com.naaturel.ANN.domain.abstraction.Trainable;
+import com.naaturel.ANN.domain.abstraction.Model;
 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.training.TrainingContext;
+import com.naaturel.ANN.implementation.simplePerceptron.*;
 import com.naaturel.ANN.domain.model.training.TrainingPipeline;
-import com.naaturel.ANN.implementation.correction.SimpleCorrectionStrategy;
-import com.naaturel.ANN.implementation.loss.SimpleLossStrategy;
 import com.naaturel.ANN.implementation.training.steps.*;
 
 import java.util.List;
@@ -19,18 +17,18 @@ public class SimpleTraining implements Trainer {
     }
 
     @Override
-    public void train(Trainable model, DataSet dataset) {
+    public void train(Model model, DataSet dataset) {
-        TrainingContext context = new TrainingContext();
+        SimpleTrainingContext context = new SimpleTrainingContext();
         context.dataset = dataset;
         context.model = model;
         context.learningRate = 0.3F;
 
         List<TrainingStep> steps = List.of(
-                new PredictionStep(),
+                new PredictionStep(new SimplePredictionStrategy(context)),
-                new DeltaStep(),
+                new DeltaStep(new SimpleDeltaStrategy(context)),
-                new LossStep(new SimpleLossStrategy()),
+                new LossStep(new SimpleLossStrategy(context)),
-                new SimpleErrorDetectionStep(),
+                new ErrorRegistrationStep(new SimpleErrorRegistrationStrategy(context)),
-                new WeightCorrectionStep(new SimpleCorrectionStrategy())
+                new WeightCorrectionStep(new SimpleCorrectionStrategy(context))
         );
 
         TrainingPipeline pipeline = new TrainingPipeline(steps);

src/main/java/com/naaturel/ANN/implementation/training/steps/DeltaStep.java

@@ -1,19 +1,22 @@
 package com.naaturel.ANN.implementation.training.steps;
 
+import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+import com.naaturel.ANN.domain.abstraction.TrainingContext;
 import com.naaturel.ANN.domain.abstraction.TrainingStep;
 import com.naaturel.ANN.domain.model.dataset.DataSet;
 import com.naaturel.ANN.domain.model.dataset.DataSetEntry;
 import com.naaturel.ANN.domain.model.dataset.Label;
-import com.naaturel.ANN.domain.model.training.TrainingContext;
 
 public class DeltaStep implements TrainingStep {
 
-    @Override
+    private final AlgorithmStrategy strategy;
-    public void run(TrainingContext ctx) {
-        DataSet dataSet = ctx.dataset;
-        DataSetEntry entry = ctx.currentEntry;
-        Label label = dataSet.getLabel(entry);
 
-        ctx.delta = label.getValue() - ctx.prediction;
+    public DeltaStep(AlgorithmStrategy strategy) {
+        this.strategy = strategy;
+    }
+
+    @Override
+    public void run() {
+        this.strategy.apply();
     }
 }

src/main/java/com/naaturel/ANN/implementation/training/steps/ErrorRegistrationStep.java

@@ -0,0 +1,18 @@
+package com.naaturel.ANN.implementation.training.steps;
+
+import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+import com.naaturel.ANN.domain.abstraction.TrainingStep;
+
+public class ErrorRegistrationStep implements TrainingStep {
+
+    private final AlgorithmStrategy strategy;
+
+    public ErrorRegistrationStep(AlgorithmStrategy strategy) {
+        this.strategy = strategy;
+    }
+
+    @Override
+    public void run() {
+        this.strategy.apply();
+    }
+}

src/main/java/com/naaturel/ANN/implementation/training/steps/LossStep.java

@@ -1,11 +1,12 @@
 package com.naaturel.ANN.implementation.training.steps;
 
 import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+import com.naaturel.ANN.domain.abstraction.TrainingContext;
 import com.naaturel.ANN.domain.abstraction.TrainingStep;
-import com.naaturel.ANN.domain.model.training.TrainingContext;
 
 public class LossStep implements TrainingStep {
 
+
     private final AlgorithmStrategy lossStrategy;
 
     public LossStep(AlgorithmStrategy strategy) {
@@ -13,7 +14,7 @@ public class LossStep implements TrainingStep {
     }
 
     @Override
-    public void run(TrainingContext ctx) {
+    public void run() {
-        this.lossStrategy.apply(ctx);
+        this.lossStrategy.apply();
     }
 }

src/main/java/com/naaturel/ANN/implementation/training/steps/PredictionStep.java

@@ -1,17 +1,23 @@
 package com.naaturel.ANN.implementation.training.steps;
 
+import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
+import com.naaturel.ANN.domain.abstraction.TrainingContext;
 import com.naaturel.ANN.domain.abstraction.TrainingStep;
-import com.naaturel.ANN.domain.model.neuron.Input;
+import com.naaturel.ANN.implementation.simplePerceptron.SimplePredictionStrategy;
-import com.naaturel.ANN.domain.model.training.TrainingContext;
+import com.naaturel.ANN.implementation.simplePerceptron.SimpleTrainingContext;
 
-import java.util.ArrayList;
 import java.util.List;
 
 public class PredictionStep implements TrainingStep {
 
+    private final SimplePredictionStrategy strategy;
+
+    public PredictionStep(SimplePredictionStrategy strategy) {
+        this.strategy = strategy;
+    }
+
     @Override
-    public void run(TrainingContext ctx) {
+    public void run() {
-        List<Float> predictions = ctx.model.predict(ctx.currentEntry.getData());
+        this.strategy.apply();
-        ctx.prediction = predictions.getFirst();
     }
 }

src/main/java/com/naaturel/ANN/implementation/training/steps/SimpleErrorDetectionStep.java

@@ -1,13 +0,0 @@
-package com.naaturel.ANN.implementation.training.steps;
-
-import com.naaturel.ANN.domain.abstraction.TrainingStep;
-import com.naaturel.ANN.domain.model.training.TrainingContext;
-
-public class SimpleErrorDetectionStep implements TrainingStep {
-
-    @Override
-    public void run(TrainingContext ctx) {
-        ctx.globalLoss += ctx.localLoss;
-    }
-
-}

src/main/java/com/naaturel/ANN/implementation/training/steps/WeightCorrectionStep.java

@@ -2,7 +2,6 @@ package com.naaturel.ANN.implementation.training.steps;
 
 import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
 import com.naaturel.ANN.domain.abstraction.TrainingStep;
-import com.naaturel.ANN.domain.model.training.TrainingContext;
 
 public class WeightCorrectionStep implements TrainingStep {
 
@@ -13,7 +12,7 @@ public class WeightCorrectionStep implements TrainingStep {
     }
 
     @Override
-    public void run(TrainingContext ctx) {
+    public void run() {
-        this.correctionStrategy.apply(ctx);
+        this.correctionStrategy.apply();
     }
 }