Start to reimplement gradient descent

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

@@ -5,6 +5,7 @@ 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;
@@ -15,49 +16,14 @@ import com.naaturel.ANN.implementation.neuron.SimplePerceptron;
 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 orDataSet = new DataSet(Map.ofEntries(
+        DataSet dataset = new DatasetExtractor().extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/or.csv");
-                Map.entry(new DataSetEntry(List.of(0.0F, 0.0F)), new Label(0.0F)),
-                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(1.0F, 1.0F)), new Label(1.0F))
-        ));
-
-        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(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))
-        ));
-
-        DataSet dataSet = new DataSet(Map.ofEntries(
-                Map.entry(new DataSetEntry(List.of(1.0F, 6.0F)), new Label(1.0F)),
-                Map.entry(new DataSetEntry(List.of(7.0F, 9.0F)), new Label(-1.0F)),
-                Map.entry(new DataSetEntry(List.of(1.0F, 9.0F)), new Label(1.0F)),
-                Map.entry(new DataSetEntry(List.of(7.0F, 10.0F)), new Label(-1.0F)),
-                Map.entry(new DataSetEntry(List.of(2.0F, 5.0F)), new Label(-1.0F)),
-                Map.entry(new DataSetEntry(List.of(2.0F, 7.0F)), new Label(1.0F)),
-                Map.entry(new DataSetEntry(List.of(2.0F, 8.0F)), new Label(1.0F)),
-                Map.entry(new DataSetEntry(List.of(6.0F, 8.0F)), new Label(-1.0F)),
-                Map.entry(new DataSetEntry(List.of(6.0F, 9.0F)), new Label(-1.0F)),
-                Map.entry(new DataSetEntry(List.of(3.0F, 5.0F)), new Label(-1.0F)),
-                Map.entry(new DataSetEntry(List.of(3.0F, 6.0F)), new Label(-1.0F)),
-                Map.entry(new DataSetEntry(List.of(3.0F, 8.0F)), new Label(1.0F)),
-                Map.entry(new DataSetEntry(List.of(3.0F, 9.0F)), new Label(1.0F)),
-                Map.entry(new DataSetEntry(List.of(5.0F, 7.0F)), new Label(-1.0F)),
-                Map.entry(new DataSetEntry(List.of(5.0F, 8.0F)), new Label(-1.0F)),
-                Map.entry(new DataSetEntry(List.of(5.0F, 10.0F)), new Label(1.0F)),
-                Map.entry(new DataSetEntry(List.of(5.0F, 11.0F)), new Label(1.0F)),
-                Map.entry(new DataSetEntry(List.of(4.0F, 6.0F)), new Label(-1.0F)),
-                Map.entry(new DataSetEntry(List.of(4.0F, 7.0F)), new Label(-1.0F)),
-                Map.entry(new DataSetEntry(List.of(4.0F, 9.0F)), new Label(1.0F)),
-                Map.entry(new DataSetEntry(List.of(4.0F, 10.0F)), new Label(1.0F))
-        ));
 
         List<Synapse> syns = new ArrayList<>();
         syns.add(new Synapse(new Input(0), new Weight(0)));
@@ -70,7 +36,7 @@ public class Main {
         Network network = new Network(List.of(layer));
 
         Trainer trainer = new SimpleTraining();
-        trainer.train(network, orDataSet);
+        trainer.train(network, dataset);
 
     }
 }

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

@@ -2,8 +2,8 @@ package com.naaturel.ANN.domain.abstraction;
 
 import com.naaturel.ANN.domain.model.training.TrainingContext;
 
-public interface CorrectionStrategy {
+public interface AlgorithmStrategy {
 
-    void apply(TrainingContext context);
+    void apply(TrainingContext ctx);
 
 }

src/main/java/com/naaturel/ANN/domain/model/dataset/DataSet.java

@@ -1,12 +1,14 @@
 package com.naaturel.ANN.domain.model.dataset;
 
+import com.naaturel.ANN.domain.model.neuron.Input;
+
 import java.util.*;
 
 public class DataSet implements Iterable<DataSetEntry>{
 
     private Map<DataSetEntry, Label> data;
 
-    public DataSet(){
+    public DataSet() {
         this(new HashMap<>());
     }
 
@@ -31,15 +33,17 @@ public class DataSet implements Iterable<DataSetEntry>{
 
         float maxAbs = entries.stream()
                 .flatMap(e -> e.getData().stream())
+                .map(Input::getValue)
                 .map(Math::abs)
                 .max(Float::compare)
                 .orElse(1.0F);
 
         Map<DataSetEntry, Label> normalized = new HashMap<>();
         for (DataSetEntry entry : entries) {
-            List<Float> normalizedData = new ArrayList<>();
+            List<Input> normalizedData = new ArrayList<>();
-            for (float value : entry.getData()) {
+            for (Input input : entry.getData()) {
-                normalizedData.add(Math.round((value / maxAbs) * 100.0F) / 100.0F);
+                Input normalizedInput = new Input(Math.round((input.getValue() / maxAbs) * 100.0F) / 100.0F);
+                normalizedData.add(normalizedInput);
             }
             normalized.put(new DataSetEntry(normalizedData), this.data.get(entry));
         }

src/main/java/com/naaturel/ANN/domain/model/dataset/DataSetEntry.java

@@ -1,16 +1,18 @@
 package com.naaturel.ANN.domain.model.dataset;
 
+import com.naaturel.ANN.domain.model.neuron.Input;
+
 import java.util.*;
 
-public class DataSetEntry implements Iterable<Float> {
+public class DataSetEntry implements Iterable<Input> {
 
-    private List<Float> data;
+    private List<Input> data;
 
-    public DataSetEntry(List<Float> data){
+    public DataSetEntry(List<Input> data){
         this.data = data;
     }
 
-    public List<Float> getData() {
+    public List<Input> getData() {
         return new ArrayList<>(data);
     }
 
@@ -28,7 +30,7 @@ public class DataSetEntry implements Iterable<Float> {
     }
 
     @Override
-    public Iterator<Float> iterator() {
+    public Iterator<Input> iterator() {
         return this.data.iterator();
     }
 

src/main/java/com/naaturel/ANN/domain/model/dataset/DatasetExtractor.java

@@ -0,0 +1,36 @@
+package com.naaturel.ANN.domain.model.dataset;
+
+import com.naaturel.ANN.domain.model.neuron.Input;
+
+import java.io.BufferedReader;
+import java.io.FileReader;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
+
+public class DatasetExtractor {
+
+    public DataSet extract(String path) {
+        Map<DataSetEntry, Label> data = new HashMap<>();
+
+        try (BufferedReader reader = new BufferedReader(new FileReader(path))) {
+            String line;
+            while ((line = reader.readLine()) != null) {
+                String[] parts = line.split(",");
+                List<Input> inputs = new ArrayList<>();
+                for (int i = 0; i < parts.length - 1; i++) {
+                    inputs.add(new Input(Float.parseFloat(parts[i].trim())));
+                }
+                float label = Float.parseFloat(parts[parts.length - 1].trim());
+                data.put(new DataSetEntry(inputs), new Label(label));
+            }
+        } catch (IOException e) {
+            throw new RuntimeException("Failed to read dataset from: " + path, e);
+        }
+
+        return new DataSet(data);
+    }
+
+}

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

@@ -1,9 +1,21 @@
 package com.naaturel.ANN.implementation.correction;
 
-import com.naaturel.ANN.domain.abstraction.CorrectionStrategy;
+import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
 import com.naaturel.ANN.domain.model.training.TrainingContext;
 
-public class GradientDescentCorrectionStrategy implements CorrectionStrategy {
+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/correction/SimpleCorrectionStrategy.java

@@ -1,9 +1,9 @@
 package com.naaturel.ANN.implementation.correction;
 
-import com.naaturel.ANN.domain.abstraction.CorrectionStrategy;
+import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
 import com.naaturel.ANN.domain.model.training.TrainingContext;
 
-public class SimpleCorrectionStrategy implements CorrectionStrategy {
+public class SimpleCorrectionStrategy implements AlgorithmStrategy {
 
     @Override
     public void apply(TrainingContext context) {

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

@@ -0,0 +1,11 @@
+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

@@ -0,0 +1,11 @@
+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/neuron/SimplePerceptron.java

@@ -1,13 +1,10 @@
 package com.naaturel.ANN.implementation.neuron;
 
 import com.naaturel.ANN.domain.abstraction.ActivationFunction;
-import com.naaturel.ANN.domain.abstraction.CorrectionStrategy;
 import com.naaturel.ANN.domain.abstraction.Neuron;
-import com.naaturel.ANN.domain.abstraction.Trainable;
 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.List;
 import java.util.function.Consumer;

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

@@ -1,25 +1,50 @@
 package com.naaturel.ANN.implementation.training;
 
-import com.naaturel.ANN.domain.abstraction.Neuron;
+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.dataset.DataSetEntry;
+import com.naaturel.ANN.domain.model.training.TrainingContext;
-import com.naaturel.ANN.domain.model.neuron.Bias;
+import com.naaturel.ANN.domain.model.training.TrainingPipeline;
-import com.naaturel.ANN.domain.model.neuron.Input;
+import com.naaturel.ANN.implementation.correction.GradientDescentCorrectionStrategy;
-import com.naaturel.ANN.domain.model.neuron.Synapse;
+import com.naaturel.ANN.implementation.correction.SimpleCorrectionStrategy;
-import com.naaturel.ANN.domain.model.neuron.Weight;
+import com.naaturel.ANN.implementation.loss.SquareLossStrategy;
+import com.naaturel.ANN.implementation.training.steps.*;
 
-import java.util.ArrayList;
-import java.util.Arrays;
 import java.util.List;
 
-/*public class GradientDescentTraining implements Trainer {
+public class GradientDescentTraining implements Trainer {
 
     public GradientDescentTraining(){
 
     }
 
-    public void train(Neuron n, float learningRate, DataSet dataSet) {
+    @Override
+    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 LossStep(new SquareLossStrategy()),
+                new SimpleErrorDetectionStep(),
+                new WeightCorrectionStep(new GradientDescentCorrectionStrategy(2))
+        );
+
+        TrainingPipeline pipeline = new TrainingPipeline(steps);
+        pipeline
+                .stopCondition(ctx -> ctx.globalLoss == 0.0F || ctx.epoch > 100)
+                .beforeEpoch(ctx -> ctx.globalLoss = 0)
+                .afterEpoch(ctx -> ())
+                .withVerbose(true)
+                .withTimeMeasurement(true)
+                .run(context);
+    }
+
+    /*public void train(Neuron n, float learningRate, DataSet dataSet) {
         int epoch = 1;
         int maxEpoch = 402;
         float errorThreshold = 0F;
@@ -120,6 +145,6 @@ import java.util.List;
         variance /= dataSet.size();
 
         return variance;
-    }
+    }*/
 
-}*/
+}

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

@@ -7,6 +7,7 @@ import com.naaturel.ANN.domain.model.dataset.DataSet;
 import com.naaturel.ANN.domain.model.training.TrainingContext;
 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;
@@ -27,7 +28,7 @@ public class SimpleTraining implements Trainer {
         List<TrainingStep> steps = List.of(
                 new PredictionStep(),
                 new DeltaStep(),
-                new SimpleLossStep(),
+                new LossStep(new SimpleLossStrategy()),
                 new SimpleErrorDetectionStep(),
                 new WeightCorrectionStep(new SimpleCorrectionStrategy())
         );

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

@@ -0,0 +1,19 @@
+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 LossStep implements TrainingStep {
+
+    private final AlgorithmStrategy lossStrategy;
+
+    public LossStep(AlgorithmStrategy strategy) {
+        this.lossStrategy = strategy;
+    }
+
+    @Override
+    public void run(TrainingContext ctx) {
+        this.lossStrategy.apply(ctx);
+    }
+}

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

@@ -11,11 +11,7 @@ public class PredictionStep implements TrainingStep {
 
     @Override
     public void run(TrainingContext ctx) {
-        List<Input> inputs = new ArrayList<>();
+        List<Float> predictions = ctx.model.predict(ctx.currentEntry.getData());
-        for(Float f : ctx.currentEntry.getData()){
-            inputs.add(new Input(f));
-        }
-        List<Float> predictions = ctx.model.predict(inputs);
         ctx.prediction = predictions.getFirst();
     }
 }

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

@@ -1,12 +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 SimpleLossStep implements TrainingStep {
-
-    @Override
-    public void run(TrainingContext ctx) {
-        ctx.localLoss = Math.abs(ctx.delta);
-    }
-}

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

@@ -1,14 +1,14 @@
 package com.naaturel.ANN.implementation.training.steps;
 
-import com.naaturel.ANN.domain.abstraction.CorrectionStrategy;
+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 {
 
-    private final CorrectionStrategy correctionStrategy;
+    private final AlgorithmStrategy correctionStrategy;
 
-    public WeightCorrectionStep(CorrectionStrategy strategy) {
+    public WeightCorrectionStep(AlgorithmStrategy strategy) {
         this.correctionStrategy = strategy;
     }