Start to add test coverage

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

@@ -20,8 +20,8 @@ public class Main {
         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");
+        DataSet andDataset = new DatasetExtractor()
+                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and.csv");
 
         List<Synapse> syns = new ArrayList<>();
         syns.add(new Synapse(new Input(0), new Weight(0)));
@@ -29,12 +29,12 @@ public class Main {
 
         Bias bias =  new Bias(new Weight(0));
 
-        Neuron neuron = new SimplePerceptron(syns, bias, new Linear());
+        Neuron neuron = new SimplePerceptron(syns, bias, new Heaviside());
         Layer layer = new Layer(List.of(neuron));
         Network network = new Network(List.of(layer));
 
-        Trainer trainer = new GradientDescentTraining();
-        trainer.train(network, dataset);
+        Trainer trainer = new SimpleTraining();
+        trainer.train(network, andDataset);
 
     }
 }

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

@@ -1,5 +1,6 @@
 package com.naaturel.ANN.domain.abstraction;
 
+@FunctionalInterface
 public interface AlgorithmStrategy {
 
     void apply();

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

@@ -8,7 +8,7 @@ import java.util.function.Consumer;
 
 public interface Model {
     int synCount();
-    void applyOnSynapses(Consumer<Synapse> consumer);
+    void forEachSynapse(Consumer<Synapse> consumer);
     List<Float> predict(List<Input> inputs);
 
 }

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

@@ -9,7 +9,7 @@ public class DataSet implements Iterable<DataSetEntry>{
     private Map<DataSetEntry, Label> data;
 
     public DataSet() {
-        this(new HashMap<>());
+        this(new LinkedHashMap<>());
     }
 
     public DataSet(Map<DataSetEntry, Label> data){

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

@@ -5,15 +5,12 @@ 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;
+import java.util.*;
 
 public class DatasetExtractor {
 
     public DataSet extract(String path) {
-        Map<DataSetEntry, Label> data = new HashMap<>();
+        Map<DataSetEntry, Label> data = new LinkedHashMap<>();
 
         try (BufferedReader reader = new BufferedReader(new FileReader(path))) {
             String line;

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

@@ -35,7 +35,7 @@ public class Layer implements Model {
     }
 
     @Override
-    public void applyOnSynapses(Consumer<Synapse> consumer) {
-        this.neurons.forEach(neuron -> neuron.applyOnSynapses(consumer));
+    public void forEachSynapse(Consumer<Synapse> consumer) {
+        this.neurons.forEach(neuron -> neuron.forEachSynapse(consumer));
     }
 }

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

@@ -34,7 +34,7 @@ public class Network implements Model {
     }
 
     @Override
-    public void applyOnSynapses(Consumer<Synapse> consumer) {
-        this.layers.forEach(layer -> layer.applyOnSynapses(consumer));
+    public void forEachSynapse(Consumer<Synapse> consumer) {
+        this.layers.forEach(layer -> layer.forEachSynapse(consumer));
     }
 }

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

@@ -56,9 +56,6 @@ 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));
     }
 
@@ -77,6 +74,9 @@ public class TrainingPipeline {
                 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/gradientDescent/GradientDescentCorrectionStrategy.java

@@ -15,7 +15,7 @@ public class GradientDescentCorrectionStrategy implements AlgorithmStrategy {
     @Override
     public void apply() {
         AtomicInteger i = new AtomicInteger(0);
-        context.model.applyOnSynapses(syn -> {
+        context.model.forEachSynapse(syn -> {
             float corrector = context.correctorTerms.get(i.get());
             float c = syn.getWeight() + corrector;
             syn.setWeight(c);

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

@@ -16,7 +16,7 @@ public class GradientDescentErrorStrategy implements AlgorithmStrategy {
     @Override
     public void apply() {
         AtomicInteger i = new AtomicInteger(0);
-        context.model.applyOnSynapses(syn -> {
+        context.model.forEachSynapse(syn -> {
             float corrector = context.correctorTerms.get(i.get());
             corrector += context.learningRate * context.delta * syn.getInput();
             context.correctorTerms.set(i.get(), corrector);

src/main/java/com/naaturel/ANN/implementation/neuron/SimplePerceptron.java

@@ -22,7 +22,7 @@ public class SimplePerceptron extends Neuron {
     }
 
     @Override
-    public void applyOnSynapses(Consumer<Synapse> consumer) {
+    public void forEachSynapse(Consumer<Synapse> consumer) {
         consumer.accept(this.bias);
         this.synapses.forEach(consumer);
     }
@@ -30,10 +30,10 @@ public class SimplePerceptron extends Neuron {
     @Override
     public float calculateWeightedSum() {
         float res = 0;
+        res += this.bias.getWeight() * this.bias.getInput();
         for(Synapse syn : super.synapses){
             res += syn.getWeight() * syn.getInput();
         }
-        res += this.bias.getWeight() * this.bias.getInput();
         return res;
     }
 

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

@@ -12,6 +12,6 @@ public class Heaviside implements ActivationFunction {
     @Override
     public float accept(Neuron n) {
         float weightedSum = n.calculateWeightedSum();
-        return weightedSum <= 0 ? 0:1;
+        return weightedSum < 0 ? 0:1;
     }
 }

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

@@ -2,6 +2,7 @@ package com.naaturel.ANN.implementation.simplePerceptron;
 
 import com.naaturel.ANN.domain.abstraction.AlgorithmStrategy;
 
+
 public class SimpleCorrectionStrategy implements AlgorithmStrategy {
 
     private final SimpleTrainingContext context;
@@ -13,7 +14,7 @@ public class SimpleCorrectionStrategy implements AlgorithmStrategy {
     @Override
     public void apply() {
         if(context.currentLabel.getValue() == context.prediction) return ;
-        context.model.applyOnSynapses(syn -> {
+        context.model.forEachSynapse(syn -> {
             float currentW = syn.getWeight();
             float currentInput  = syn.getInput();
             float newValue = currentW + (context.learningRate * context.delta * currentInput);

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

@@ -27,7 +27,7 @@ public class GradientDescentTraining implements Trainer {
         GradientDescentTrainingContext context = new GradientDescentTrainingContext();
         context.dataset = dataset;
         context.model = model;
-        context.learningRate = 0.00011F;
+        context.learningRate = 0.2F;
         context.correctorTerms =  new ArrayList<>();
 
         List<TrainingStep> steps = List.of(
@@ -40,7 +40,7 @@ public class GradientDescentTraining implements Trainer {
 
         TrainingPipeline pipeline = new TrainingPipeline(steps);
         pipeline
-                .stopCondition(ctx -> ctx.globalLoss == 0.0F || ctx.epoch > 1000)
+                .stopCondition(ctx -> ctx.globalLoss == 0.0F || ctx.epoch > 50)
                 .beforeEpoch(ctx -> {
                     ctx.globalLoss = 0.0F;
                     for (int i = 0; i < model.synCount(); i++){

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

@@ -33,7 +33,7 @@ public class SimpleTraining implements Trainer {
 
         TrainingPipeline pipeline = new TrainingPipeline(steps);
         pipeline
-                .stopCondition(ctx -> ctx.globalLoss == 0.0F || ctx.epoch > 100)
+                .stopCondition(ctx -> ctx.globalLoss == 0.0F || ctx.epoch > 10)
                 .beforeEpoch(ctx -> ctx.globalLoss = 0)
                 .withVerbose(true)
                 .run(context);

src/test/java/perceptron/simplePerceptronTest.java

@@ -0,0 +1,85 @@
+package perceptron;
+
+import com.naaturel.ANN.domain.abstraction.Neuron;
+import com.naaturel.ANN.domain.abstraction.TrainingStep;
+import com.naaturel.ANN.domain.model.dataset.DataSet;
+import com.naaturel.ANN.domain.model.dataset.DatasetExtractor;
+import com.naaturel.ANN.domain.model.neuron.*;
+import com.naaturel.ANN.domain.model.training.TrainingPipeline;
+import com.naaturel.ANN.implementation.neuron.SimplePerceptron;
+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;
+
+import java.util.ArrayList;
+import java.util.List;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+
+public class simplePerceptronTest {
+
+    private DataSet dataset;
+    private SimpleTrainingContext context;
+
+    private List<Synapse> synapses;
+    private Bias bias;
+    private Network network;
+
+    private TrainingPipeline pipeline;
+
+    @BeforeEach
+    public void init(){
+        dataset = new DatasetExtractor()
+                .extract("C:/Users/Laurent/Desktop/ANN-framework/src/main/resources/assets/and.csv");
+
+        List<Synapse> syns = new ArrayList<>();
+        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));
+
+        Neuron neuron = new SimplePerceptron(syns, bias, new Heaviside());
+        Layer layer = new Layer(List.of(neuron));
+        network = new Network(List.of(layer));
+
+        context = new SimpleTrainingContext();
+        context.dataset = dataset;
+        context.model = network;
+
+        List<TrainingStep> steps = List.of(
+                new PredictionStep(new SimplePredictionStrategy(context)),
+                new DeltaStep(new SimpleDeltaStrategy(context)),
+                new LossStep(new SimpleLossStrategy(context)),
+                new ErrorRegistrationStep(new SimpleErrorRegistrationStrategy(context)),
+                new WeightCorrectionStep(new SimpleCorrectionStrategy(context))
+        );
+
+        pipeline = new TrainingPipeline(steps);
+        pipeline.stopCondition(ctx -> ctx.globalLoss == 0.0F || ctx.epoch > 100);
+        pipeline.beforeEpoch(ctx -> ctx.globalLoss = 0);
+    }
+
+    @Test
+    public void test_the_whole_algorithm(){
+
+        List<Float> expectedGlobalLosses = List.of(
+                2.0F,
+                3.0F,
+                3.0F,
+                2.0F,
+                1.0F,
+                0.0F
+        );
+
+        context.learningRate = 1F;
+        pipeline.afterEpoch(ctx -> {
+            int index = ctx.epoch-1;
+            assertEquals(expectedGlobalLosses.get(index), context.globalLoss);
+        });
+
+        pipeline.run(context);
+        assertEquals(6, context.epoch);
+    }
+}