Add gradient descent test

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

@@ -27,29 +27,27 @@ public class GradientDescentTraining implements Trainer {
         GradientDescentTrainingContext context = new GradientDescentTrainingContext();
         context.dataset = dataset;
         context.model = model;
-        context.learningRate = 0.2F;
         context.correctorTerms =  new ArrayList<>();
 
         List<TrainingStep> steps = List.of(
                 new PredictionStep(new SimplePredictionStrategy(context)),
                 new DeltaStep(new SimpleDeltaStrategy(context)),
                 new LossStep(new SquareLossStrategy(context)),
-                new ErrorRegistrationStep(new GradientDescentErrorStrategy(context)),
-                new WeightCorrectionStep(new GradientDescentCorrectionStrategy(context))
+                new ErrorRegistrationStep(new GradientDescentErrorStrategy(context))
         );
 
-        TrainingPipeline pipeline = new TrainingPipeline(steps);
-        pipeline
-                .stopCondition(ctx -> ctx.globalLoss == 0.0F || ctx.epoch > 50)
+        new TrainingPipeline(steps)
+            .stopCondition(ctx -> ctx.globalLoss <= 0.125F || ctx.epoch > 100)
             .beforeEpoch(ctx -> {
-                    ctx.globalLoss = 0.0F;
-                    for (int i = 0; i < model.synCount(); i++){
-                        context.correctorTerms.add(0F);
+                GradientDescentTrainingContext gdCtx = (GradientDescentTrainingContext) ctx;
+                gdCtx.globalLoss = 0.0F;
+                gdCtx.correctorTerms.clear();
+                for (int i = 0; i < ctx.model.synCount(); i++){
+                    gdCtx.correctorTerms.add(0F);
                 }
             })
-                .afterEpoch(ctx -> ctx.globalLoss /= ctx.dataset.size())
+            .afterEpoch(ctx -> new GradientDescentCorrectionStrategy(context).apply())
             .withVerbose(true)
-                .withTimeMeasurement(true)
             .run(context);
     }
 

src/main/resources/assets/and-gradient.csv

@@ -0,0 +1,4 @@
+0,0,-1
+0,1,-1
+1,0,-1
+1,1,1

src/test/java/gradientDescent/GradientDescentTest.java

@@ -0,0 +1,98 @@
+package gradientDescent;
+
+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.gradientDescent.*;
+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 java.util.concurrent.atomic.AtomicInteger;
+
+import static org.junit.jupiter.api.Assertions.*;
+
+
+public class GradientDescentTest {
+
+    private DataSet dataset;
+    private GradientDescentTrainingContext 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-gradient.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 Linear());
+        Layer layer = new Layer(List.of(neuron));
+        network = new Network(List.of(layer));
+
+        context = new GradientDescentTrainingContext();
+        context.dataset = dataset;
+        context.model = network;
+        context.correctorTerms =  new ArrayList<>();
+
+        List<TrainingStep> steps = List.of(
+                new PredictionStep(new SimplePredictionStrategy(context)),
+                new DeltaStep(new SimpleDeltaStrategy(context)),
+                new LossStep(new SquareLossStrategy(context)),
+                new ErrorRegistrationStep(new GradientDescentErrorStrategy(context))
+        );
+
+        pipeline = new TrainingPipeline(steps)
+                .stopCondition(ctx -> ctx.globalLoss <= 0.125F || ctx.epoch > 100)
+                .beforeEpoch(ctx -> {
+                    GradientDescentTrainingContext gdCtx = (GradientDescentTrainingContext) ctx;
+                    gdCtx.globalLoss = 0.0F;
+                    gdCtx.correctorTerms.clear();
+                    for (int i = 0; i < ctx.model.synCount(); i++){
+                        gdCtx.correctorTerms.add(0F);
+                    }
+                });
+    }
+
+    @Test
+    public void test_the_whole_algorithm(){
+
+        List<Float> expectedGlobalLosses = List.of(
+                0.5F,
+                0.38F,
+                0.3176F,
+                0.272096F,
+                0.237469F
+        );
+
+        context.learningRate = 0.2F;
+        pipeline.afterEpoch(ctx -> {
+            context.globalLoss /= context.dataset.size();
+            new GradientDescentCorrectionStrategy(context).apply();
+
+            int index = ctx.epoch-1;
+            if(index >= expectedGlobalLosses.size()) return;
+
+            assertEquals(expectedGlobalLosses.get(index), context.globalLoss, 0.00001f);
+        });
+
+        pipeline.run(context);
+        assertEquals(67, context.epoch);
+    }
+}

src/test/java/perceptron/SimplePerceptronTest.java

@@ -18,7 +18,7 @@ import java.util.List;
 import static org.junit.jupiter.api.Assertions.*;
 
 
-public class simplePerceptronTest {
+public class SimplePerceptronTest {
 
     private DataSet dataset;
     private SimpleTrainingContext context;